1. Bibliographic Information

1.1. Title

CreativeVR: Diffusion-Prior-Guided Approach for Structure and Motion Restoration in Generative and Real Videos

1.2. Authors

Ishan Rajendrakumar Dave, Chongjian Ge, Ersin Yumer, Xue Bai (all from Adobe, with Ishan Rajendrakumar Dave also from the University of Massachusetts Amherst). The authors' affiliations indicate a collaboration between industry research (Adobe) and academia (University of Massachusetts Amherst), common in cutting-edge AI research.

1.3. Journal/Conference

The paper was published on arXiv, a preprint server for scientific papers, with a publication date of 2025-12-12T22:03:14.000Z. While not a peer-reviewed journal or conference yet, arXiv is widely used in the machine learning community for early dissemination of research. Given the advanced topic and affiliations, it is likely intended for a top-tier computer vision or machine learning conference (e.g., CVPR, ICCV, NeurIPS, ICLR) in the future.

1.4. Publication Year

2025

1.5. Abstract

The paper addresses the significant challenge of structural and temporal artifacts in modern AI-generated (AIGC) videos (e.g., distorted faces, warped backgrounds, inconsistent motion) and very low-quality real-world videos. It highlights that traditional video restoration (VR) methods often stabilize these artifacts rather than repair them, and existing diffusion-prior restorers lack fine-grained control over perceptual quality vs. fidelity. The authors introduce CreativeVR, a diffusion-prior-guided video restoration framework for both AIGC and real videos. The core of CreativeVR is a deep-adapter-based method that incorporates a single precision knob, allowing users to smoothly adjust the trade-off between strict adherence to the input and more aggressive structure- and motion-corrective behaviors. A key innovation is a temporally coherent degradation module used during training, which synthesizes realistic structural failures. To evaluate AIGC artifact restoration, CreativeVR proposes the AIGC54 benchmark using FIQA, semantic, and perceptual metrics. CreativeVR achieves state-of-the-art results on videos with severe artifacts, performs competitively on standard VR benchmarks, and operates at practical speeds (around 13 FPS at 720p).

1.6. Original Source Link

https://arxiv.org/abs/2512.12060 PDF Link: https://arxiv.org/pdf/2512.12060v1.pdf This is a preprint on arXiv, not yet formally published in a peer-reviewed venue.

2. Executive Summary

2.1. Background & Motivation

The core problem addressed by the paper is the pervasive issue of structural and temporal artifacts in AI-generated content (AIGC) videos and low-quality real-world footage.

• AI-Generated Videos: Modern text-to-video (T2V) diffusion models can produce visually compelling clips, but they struggle with fine-scale details. Even state-of-the-art models often generate distorted faces and hands, warped backgrounds, cross-frame drift, missing object permanence, and temporally inconsistent motion. These imperfections prevent their adoption in professional production pipelines, where costly and non-deterministic re-generation is undesirable. This necessitates a dedicated refinement stage.
• Real-World Videos: Similar severe artifacts appear in real-world videos, including legacy archives, low-quality smartphone footage (due to compression or low light), and damaged material.
• Limitations of Existing Solutions:

  • Classical Video Restoration (VR)/Super-Resolution (VSR) methods: These are designed for synthetic degradations like blur and downsampling. They lack semantic understanding and tend to stabilize existing artifacts (e.g., a malformed hand) rather than semantically repair them.

  • Post-hoc fixes for T2V outputs (e.g., GANs, diffusion upsamplers/refiners): These often either hallucinate new content that deviates from the original intent or are too domain-specific, failing to generalize across AIGC and real footage.

  • Diffusion-prior restorers: While improving perceptual quality, they are typically trained on photometric noise (e.g., simple Gaussian noise) and lack mechanisms to control the trade-off between perceptual quality and fidelity. This can lead to sharpening textures without fixing geometry or over-correcting and drifting from the source.

    The paper's entry point is to explicitly target this trade-off by developing a method that can leverage strong generative priors while still being guided by the degraded input, providing a flexible control mechanism to correct severe structural and motion artifacts in both AIGC and real videos.

2.2. Main Contributions / Findings

The paper makes several significant contributions:

1. CreativeVR Framework: Introduction of CreativeVR, a diffusion-prior-guided deep adapter built on a frozen Text-to-Video Diffusion Transformer (T2V DiT) backbone. It features a unique precision knob ($\gamma_{\ell}$) that allows smooth control over the trade-off between precise restoration for standard degradations and aggressive structure/motion-corrective refinement for challenging artifacts in both AIGC and real videos.
2. Temporally Coherent Synthetic Degradation Module: Proposal of a novel training curriculum based on a temporally coherent synthetic degradation module. This module generates realistic structural artifacts (e.g., warped faces, wobble, low-FPS blend, rolling-shutter-like distortions) by composing transformations like morphing, directional motion blur, grid-based warping, frame dropping, and spatio-temporal resampling, with parameters evolving smoothly over time to avoid unnatural flicker. This explicitly aligns the diffusion prior towards the hard failure modes observed in modern video generators.
3. AIGC-Artifact Restoration Protocol and Benchmark: Establishment of a new evaluation protocol for AIGC-artifact restoration, including the AIGC54 benchmark (comprising 54 videos from various state-of-the-art video generators) and a comprehensive metric suite. This suite combines Face Image Quality Assessment (FIQA), semantic and perceptual metrics, and GPT-based multi-aspect and arena-style preference scoring. The findings demonstrate that CreativeVR achieves state-of-the-art performance on AIGC clips (e.g., up to +37% relative FIQA improvement), outperforms existing methods on real videos with severe spatio-temporal artifacts, and remains competitive on standard VR/VSR benchmarks, all while maintaining practical throughput (approximately 13 FPS at 720p) and zero-shot generalization up to 1080p resolution.

3. Prerequisite Knowledge & Related Work

3.1. Foundational Concepts

To understand CreativeVR, a basic grasp of the following concepts is essential:

• Diffusion Models (Denoising Diffusion Probabilistic Models - DDPMs): A class of generative models that learn to reverse a diffusion process. In the forward (diffusion) process, noise is progressively added to data until it becomes pure noise. In the reverse (denoising) process, the model learns to gradually remove this noise, step by step, to reconstruct the original data. This process allows them to generate highly realistic and diverse samples. The core idea is to train a neural network (often a U-Net or Transformer) to predict the noise added at each step.
• Text-to-Video (T2V) Models: Generative models that synthesize video content from textual descriptions (prompts). These models typically combine large language models (for text understanding) with diffusion models (for video generation), often operating in a latent space for efficiency.
• Diffusion Transformers (DiT): A specific architecture for diffusion models where the U-Net backbone, traditionally used for denoising, is replaced or augmented with a Transformer architecture. Transformers are well-suited for capturing long-range dependencies in data, which is crucial for generating temporally consistent videos.
• Video Autoencoders (VAEs): Variational Autoencoders (VAEs) or more generally, Vector Quantized VAEs (VQ-VAEs) are neural networks that learn a compressed, lower-dimensional representation (latent space) of input data. For videos, a VAE encoder compresses high-resolution video frames into a compact latent representation, and a VAE decoder reconstructs the video from this latent space. This significantly reduces computational costs for diffusion models, which can then operate in the more efficient latent space.
• Video Restoration (VR) / Video Super-Resolution (VSR): These are tasks aimed at improving the quality of degraded videos. Video Restoration broadly covers tasks like denoising, deblurring, and removing artifacts. Video Super-Resolution specifically refers to increasing the resolution of a video while maintaining or enhancing its quality.
• Parameter-Efficient Fine-Tuning (PEFT) / Adapters / LoRA: Techniques designed to fine-tune large pre-trained models (like DiT backbones) with minimal computational cost and few trainable parameters.
  • Adapters: Small, lightweight neural network modules inserted into specific layers of a pre-trained model. Only these adapter modules are trained, while the main backbone remains frozen. They allow the model to adapt to new tasks or domains without significantly altering its original knowledge.
  • LoRA (Low-Rank Adaptation): A PEFT technique that injects trainable low-rank matrices into the Transformer layers of a pre-trained model. Instead of fine-tuning the entire weight matrix, only these smaller low-rank matrices are updated, drastically reducing the number of trainable parameters.

3.2. Previous Works

The paper contextualizes its contribution by discussing several categories of prior work:

• Traditional (Precise) Video Restoration:
  • Approach: These methods extend classical image super-resolution techniques to video by incorporating temporal alignment mechanisms. They focus on pixel-level fidelity.
  • Examples: EDVR [45] and TDAN [39] use deformable alignment and multi-frame fusion. BasicVSR [8] and $BasicVSR++$ [7] improve this with bidirectional propagation and larger temporal receptive fields. VideoGigaGAN [51] adapts GANs for video super-resolution.
  • Limitations: They are primarily trained for synthetic degradations (e.g., bicubic downsampling, Gaussian blur, mild compression). They lack semantic priors and fail to repair geometry-level artifacts common in AIGC (e.g., malformed limbs, identity drift, structural instability), instead tending to stabilize these artifacts.
• Diffusion Models for Video Restoration:
  • Approach: Leverage the strong generative priors of diffusion models to synthesize high-quality details under complex degradations.
  • Examples: STAR [48] augments image diffusion backbones with temporal alignment layers. VEnhancer [13] uses cross-frame attention to refine geometric structures. More recent systems like SeedVR [42], SeedVR2 [41], InfVSR [60], and FlashVideo [58] directly fine-tune high-capacity video diffusion models.
  • Limitations: These often require full fine-tuning of very large Diffusion Transformers, which is computationally expensive and risks altering the valuable motion and composition priors learned from massive text-video data. They are typically trained with photometric degradations and lack explicit mechanisms to control the trade-off between perceptual quality and fidelity, potentially sharpening textures without fixing geometry or causing over-correction and drift from the source.
• Efficient Diffusion Adaptation:
  • Approach: Focus on techniques to efficiently adapt large diffusion models to specific tasks or conditioning.
  • Examples: Adapters [15] and LoRA [16] provide lightweight specialization. ControlNet-style conditioning [56] uses separate control encoders (e.g., for edges, depth, poses). Concat-based models [21] add external control encoders. Deep adapter approaches like VACE [19] and ResTuning [20] offer modular conditioning with fewer trainable parameters.
  • Context for CreativeVR: CreativeVR builds on these by using a degradation-as-control design, where the degraded video itself acts as the internal control signal injected via lightweight adapters.

3.3. Technological Evolution

The field of video restoration has evolved from traditional signal processing-based methods, which focused on predefined degradation models (e.g., blur kernels, noise types), to deep learning-based approaches. Early deep learning methods mimicked the traditional ones but used neural networks to learn better mappings. The rise of Generative Adversarial Networks (GANs) and later Diffusion Models marked a shift towards generative priors, enabling the synthesis of plausible details that might not be directly present in the degraded input. Initially, these generative approaches were image-focused, and their extension to video often involved ensuring temporal consistency, a non-trivial task. The increasing size and complexity of generative models led to the development of Parameter-Efficient Fine-Tuning (PEFT) techniques like Adapters and LoRA, allowing for adaptation without retraining entire foundational models. CreativeVR represents a step in this evolution by combining the strong generative priors of a frozen T2V DiT with efficient deep adapters and a novel, temporally coherent degradation training strategy to specifically address the complex, semantic-level artifacts found in AIGC and real-world footage.

3.4. Differentiation Analysis

CreativeVR distinguishes itself from prior art through several key innovations:

• Targeting AIGC and Severe Real-World Artifacts: Unlike classical methods, which are tuned for synthetic degradations and lack semantic understanding to fix generative artifacts (e.g., malformed faces), CreativeVR explicitly addresses these complex structural and motion issues.
• Diffusion-Prior-Guided Control: While other diffusion-prior restorers improve perceptual quality, they often lack control over the fidelity-perceptual quality trade-off and are trained on simpler photometric degradations. CreativeVR introduces a single precision knob ($\gamma_{\ell}$) that allows users to explicitly steer how much the model relies on the degraded input versus its strong generative prior, enabling a smooth transition from precise restoration to aggressive artifact correction.
• Novel Temporally Coherent Degradation Module: Previous diffusion-based methods for restoration typically use basic photometric noise. CreativeVR introduces a sophisticated temporally coherent synthetic degradation module during training. This module creates realistic structural failures (e.g., warped faces, wobble, low-FPS blend) that mimic the hard failure modes observed in modern video generators, providing a more targeted and effective training signal for the generative prior.
• Lightweight Adaptation vs. Full Fine-tuning: Unlike many recent video diffusion model-based restorers that require full fine-tuning of large Diffusion Transformers, CreativeVR keeps the pretrained T2V DiT backbone frozen and instead trains lightweight cross-attention adapters. This significantly reduces computational costs and preserves the valuable motion and composition priors learned by the backbone from massive datasets, enabling efficient and temporally coherent restoration without altering its core generative capabilities.
• Comprehensive Evaluation for AIGC Artifacts: The paper introduces a dedicated AIGC54 benchmark and a multi-aspect metric suite (including FIQA and GPT-based evaluation) specifically designed to assess the restoration of AIGC artifacts, filling a gap in existing evaluation methodologies.

4. Methodology

4.1. Principles

The core principle behind CreativeVR is to harness the powerful generative prior of a pre-trained Text-to-Video Diffusion Transformer (T2V DiT) model to repair severe structural and motion artifacts in both AI-generated and real-world videos. Instead of fully fine-tuning this large generative model, which would be computationally expensive and risk catastrophic forgetting of its general generative capabilities, CreativeVR employs a deep-adapter-based approach. This involves keeping the DiT backbone frozen and training lightweight adapter modules that are conditioned on the degraded input video.

A crucial aspect is the explicit control over the restoration process via a precision knob. This knob allows CreativeVR to operate in two regimes: a precise restoration mode for minor degradations (similar to traditional VR/VSR) and a stronger corrective mode for severe structural and temporal artifacts. This flexibility is achieved by modulating the influence of the adapter.

Furthermore, CreativeVR introduces a novel temporally coherent synthetic degradation module during training. This module generates realistic, diverse, and temporally consistent structural artifacts that mimic the common failure modes of modern video generators and real-world low-quality footage. By training on these specific types of degradations, the model learns to explicitly correct them, leveraging the diffusion prior's ability to synthesize plausible details rather than merely stabilizing existing flaws.

4.2. Core Methodology In-depth (Layer by Layer)

The CreativeVR framework is built upon a pre-trained Text-to-Video Diffusion Transformer (T2V DiT) backbone and augmented with a deep adapter branch. The entire process involves data preparation, a diffusion denoising objective, and a unique degradation module for training.

4.2.1. Architecture Overview

The system architecture, as depicted in Figure 2, comprises:

• A frozen Text-to-Video Diffusion Transformer (DiT) backbone ($\theta$): This backbone provides a strong generative prior learned from large-scale T2V training. It is kept fixed during the CreativeVR training.

• A deep adapter branch ($\phi$): This branch is a lighter DiT with the same block design as the backbone but contains fewer blocks (e.g., half as many). It is specifically trained for the restoration task.

  The overall process can be visualized as:

  VLM Description: The image is a schematic diagram illustrating the architecture of the CreativeVR model and the workflow of its synthesis degradation module. The left side depicts the structure from the original video passing through the VAE encoder and denoising process, while the right side showcases the processes of temporal deformation, directional motion blur, and temporally consistent grid distortion. Together, these elements form a framework for repairing significant structural and motion artifacts in videos.

4.2.2. Data Flow and Latent Space Processing

1. Input Videos:

   • A clean target video, denoted as $x \in \mathbb{R}^{T \times H \times W \times 3}$, where $T$ is the number of frames, $H$ is height, $W$ is width, and 3 represents RGB channels.
   • A degraded input video, $\tilde{x}$, which is synthetically generated from $x$ using the novel degradation module: $\tilde{x} = \mathcal{D}(x; \eta)$. Here, $\mathcal{D}$ represents the degradation operator and $\eta$ are its parameters.

2. Latent Encoding: Both the clean video $x$ and its degraded counterpart $\tilde{x}$ are mapped into a shared, lower-dimensional latent space using a frozen Video Variational Autoencoder (VAE) encoder, denoted by $E$.

   • Clean latent: z = E(x)
   • Degraded latent: $\tilde{z} = E(\tilde{x})$

3. Conditioning Token Generation: The degraded latent $\tilde{z}$ is further processed by a patch embedder, $P$, to generate conditioning tokens, c = P(\tilde{z}). These tokens serve as the input for the adapter branch and guide the denoising process towards the desired restoration.

4. Noisy Latent Generation: A standard diffusion formulation is applied to the clean latent video $z$. A noise scheduler samples a timestep $t$ and adds Gaussian noise to $z$ to produce a noisy latent $z_t$. The formula for generating the noisy latent $z_t$ is: $$z_t = \alpha_t z + \sigma_t \varepsilon, \qquad \varepsilon \sim \mathcal{N}(0, I)$$

   • $z_t$: The noisy latent representation of the clean video at timestep $t$.
   • $\alpha_t$: A scalar coefficient that scales the clean latent $z$. It typically decreases from nearly 1 to 0 as $t$ progresses from 0 to $T_{max}$.
   • $z$: The clean latent representation of the original video.
   • $\sigma_t$: A scalar coefficient that scales the noise $\varepsilon$. It typically increases from nearly 0 to 1 as $t$ progresses.
   • $\varepsilon$: A sample from a standard normal distribution, $\mathcal{N}(0, I)$, representing the pure Gaussian noise added. $I$ denotes the identity matrix, implying uncorrelated noise across dimensions. This equation describes the forward diffusion process, where noise is gradually added to the clean latent.

5. Denoising with Adapter Fusion: The noisy latent $z_t$ is fed into the frozen DiT denoiser (backbone). Within a subset of its layers, the backbone features are modulated by the adapter's output.

   • Let $h_{\ell}$ be the hidden features at the $\ell$-th DiT block of the frozen backbone.
   • Let $a_{\ell} = A_{\ell}(c)$ be the output of the corresponding adapter block, $A_{\ell}$, which processes the degraded-video conditioning tokens $c$.
   • At a specified subset of layers, $\ell \in \mathcal{L}_{\mathrm{adapt}}$, the backbone and adapter features are fused using a residual modulation: $$\tilde{h}_{\ell} = h_{\ell} + \gamma_{\ell} a_{\ell}$$
   • $\tilde{h}_{\ell}$: The modulated hidden features at layer $\ell$ that are passed to the subsequent layers of the backbone.
   • $h_{\ell}$: The original hidden features from the frozen DiT backbone at layer $\ell$.
   • $\gamma_{\ell}$: A scalar precision knob that controls the adapter's influence on the backbone.
     • Small values of $\gamma_{\ell}$ mean the model relies more on its strong generative prior, suitable for aggressive artifact correction.
     • Larger values of $\gamma_{\ell}$ increase fidelity to the degraded input, leading to more precise restoration.
   • $a_{\ell}$: The output features from the $\ell$-th adapter block, $A_{\ell}$, which are derived from the degraded-video tokens $c$. This fusion mechanism allows the lightweight adapter to steer the powerful generative capabilities of the frozen backbone based on the degraded input.

4.2.3. Optimization Objective

The complete denoiser, denoted $\boldsymbol{\varepsilon}_{\theta, \phi}$, consists of the frozen backbone parameters $\theta$ and the trainable adapter parameters $\phi$. Only $\phi$ (the adapter parameters) are optimized during training. The optimization uses the standard noise-prediction diffusion loss: $$\mathcal{L}_{\mathrm{diff}}(\phi) = \mathbb{E}_{\boldsymbol{x}, t, \boldsymbol{\varepsilon}} \Big[ \big\| \boldsymbol{\varepsilon} - \boldsymbol{\varepsilon}_{\theta, \phi} (\boldsymbol{z}_t, t, c) \big\|_2^2 \Big]$$

• $\mathcal{L}_{\mathrm{diff}}(\phi)$: The diffusion loss function, which is minimized with respect to the trainable adapter parameters $\phi$.
• $\mathbb{E}_{\boldsymbol{x}, t, \boldsymbol{\varepsilon}}$: Expectation taken over the clean input video $\boldsymbol{x}$, sampled timestep $t$, and sampled noise $\boldsymbol{\varepsilon}$.
• $\boldsymbol{\varepsilon}$: The true noise vector added to the clean latent $z$ to obtain $z_t$.
• $\boldsymbol{\varepsilon}_{\theta, \phi}(\boldsymbol{z}_t, t, c)$: The noise predicted by the coupled backbone-adapter denoiser. It takes the noisy latent $z_t$, the timestep $t$, and the conditioning tokens $c$ (derived from the degraded video) as input.
• $\| \cdot \|_2^2$: The squared Euclidean norm, meaning the loss aims to minimize the mean squared error between the predicted noise and the actual noise. This loss encourages the model to accurately predict the noise, thereby learning to denoise the input back towards the clean video latent $z$, guided by the degraded input tokens $c$.

4.2.4. Synthetic Training Augmentations (Temporally Coherent Degradation Module)

A key novelty of CreativeVR is its temporally coherent synthetic degradation module, $\mathcal{D}(\boldsymbol{x}; \eta)$. This module generates realistic structural failures without unnatural flicker, providing a targeted training curriculum. Given a clean RGB clip $x = \{X_t\}_{t=1}^T$, the module produces a degraded clip $\tilde{x} = \mathcal{D}(x; \eta) = \{Y_t\}_{t=1}^T$. The parameters $\eta$ evolve smoothly over time.

The module composes several augmentations:

1. Structure-preserving Spatiotemporal Downsampling: The clip is downsampled in both time and space by factors $s_{\mathrm{temp}}$ and $s_{\mathrm{spat}}$ respectively. It is then reconstructed back to the original resolution and frame rate using simple interpolation. This mimics low frame-rate and low-resolution capture while preserving the global scene layout.

2. Temporal Morphing: This simulates exposure integration or low-FPS blending by interpolating between adjacent frames. $$Y_t = \alpha_t X_t + (1 - \alpha_t) X_{t+1}$$

   • $Y_t$: The resulting morphed frame at time $t$.
   • $X_t$: The original clean frame at time $t$.
   • $X_{t+1}$: The original clean frame at time $t+1$.
   • $\alpha_t$: A blending coefficient for frame $t$, constrained within $[\alpha_{\mathrm{min}}, \alpha_{\mathrm{max}}]$. This coefficient varies smoothly across time, resembling how real cameras integrate exposure over time.

3. Stochastic Frame Dropping: A binary drop mask $m_t \sim \mathrm{Bernoulli}(p_{\mathrm{drop}})$ is sampled, subject to a maximum run-length constraint. Frames where $m_t = 0$ are removed, and missing frames are then reconstructed via linear interpolation from the nearest retained neighbors. This simulates realistic temporal discontinuities seen in mobile or streaming videos while maintaining plausible motion trajectories.

4. Directional Motion Blur: This models exposure-integrated motion by convolving each frame with an oriented line kernel. $$Y_t = K_t(\theta_t, \ell_t) * X_t$$

   • $Y_t$: The resulting motion-blurred frame at time $t$.
   • $K_t(\theta_t, \ell_t)$: The motion blur kernel at time $t$, defined by its orientation $\theta_t$ and length $\ell_t$.
   • $\theta_t$: The orientation of the blur kernel at time $t$.
   • $\ell_t$: The length of the blur kernel at time $t$.
   • $X_t$: The original clean frame at time $t$.
   • *: The convolution operator. By allowing $\theta_t$ and $\ell_t$ to change smoothly over time, this mimics camera or object motion that produces temporally varying but coherent motion blur.

5. Grid-based Spatial Warping: To simulate effects like rolling-shutter wobble or low-frequency geometric distortions, a displacement field $d_t = (d_t^x, d_t^y)$ is generated by upsampling smooth low-resolution noise. This field is then applied via backward warping. $Y_t(u) = X_t(u + d_t(u))$

   • $Y_t(u)$: The pixel value at coordinate $u$ in the warped frame $Y_t$.
   • $X_t(u + d_t(u))$: The pixel value from the original frame $X_t$ at the displaced coordinate $u + d_t(u)$.
   • $u$: Represents pixel coordinates (x, y).
   • $d_t(u)$: The 2D displacement vector at coordinate $u$ and time $t$. The resulting elastic, wavy deformations resemble lens wobble, heat haze, or handheld jitter while keeping local geometry plausible.

6. Smooth Parameter Trajectories: All temporal parameters ($\alpha_t, \theta_t, \ell_t, d_t$) are generated from low-frequency trajectories (e.g., using sinusoidal or Perlin noise bases) and optionally smoothed with a 1D box filter. This design prevents abrupt parameter jumps and synthetic flicker, ensuring that the generated degradations evolve smoothly, similar to real-world capture effects.

4.2.5. Sampling (Inference)

At inference time, the clean branch (producing $z$) is discarded. The model conditions solely on the degraded input clip $\tilde{x}$.

1. Encode Degraded Input: $\tilde{x}$ is encoded into latents $\tilde{z} = E(\tilde{x})$.

2. Generate Conditioning Tokens: Conditioning tokens c = P(\tilde{z}) are obtained from $\tilde{z}$.

3. Run Diffusion Sampler: The diffusion sampler is run on $\tilde{z}$ (as the initial noisy latent, though it's the degraded input here) using the trained adapter and frozen backbone to produce a restored latent $\hat{z}$.

4. Decode Output: $\hat{z}$ is then decoded by the VAE decoder into the final restored video $\hat{x}$.

   For efficient sampling, the frozen T2V backbone $\theta$ is replaced with a timestep-distilled student DiT backbone $\theta_s$ (specifically, a CausVid [53] model). This plug-and-play swap allows reusing the same trained adapter blocks $A_{\ell}$ while significantly reducing the number of sampling steps (e.g., from 50 to 4), boosting throughput without degrading restoration quality. The precision knob $\gamma_{\ell}$ can be adjusted at inference time to control the trade-off.

5. Experimental Setup

5.1. Datasets

5.1.1. Training Dataset

• Mixkit split of OpenSora-Plan v1.1.0 [31]:
  • Source: An open-source large video generation model project.
  • Scale: Approximately 10,000 open-source video clips.
  • Characteristics: Spans diverse scenes, providing a rich source for learning general video generation and restoration priors.
  • Domain: General video content.
  • Preprocessing: All videos are sampled as 49-frame sequences and resized to $480 \times 832$ pixels before augmentation.
  • Purpose: To train the CreativeVR adapters and leverage the strong T2V DiT backbone's generative prior.

5.1.2. Evaluation Datasets

A. AIGC-Artifact Restoration

• AIGC54 Benchmark (Proposed by CreativeVR):
  • Source: Curated from several state-of-the-art video generators.
  • Scale: 54 videos.
  • Characteristics: Each video is a 5-second clip covering a wide variety of scenarios, including traditional dance, tourist groups, courtroom debates, crowded streets, sports events, and newsroom environments. It specifically captures challenging motion and structural artifacts typical of AIGC.
  • Domain: AI-generated content (AIGC).
  • Purpose: To specifically evaluate the model's ability to correct severe structural and temporal artifacts in videos produced by modern generative AI models.

B. Traditional Video Restoration

• REDS30 [30]:
  • Source: Realistic Dynamic Scenes dataset.
  • Characteristics: Contains diverse motion patterns and scene types, with paired low-resolution and high-resolution sequences.
  • Domain: Real-world video.
  • Purpose: Standard benchmark for video super-resolution and deblurring.
• SPMCS [52]:
  • Source: Multi-scale Progressive Fusion Video Super-Resolution dataset.
  • Characteristics: Focuses on exploiting non-local spatio-temporal correlations for video super-resolution.
  • Domain: Real-world video.
  • Purpose: Standard benchmark for video super-resolution.
• UDM10 [37]:
  • Source: Detail-revealing deep video super-resolution dataset.
  • Characteristics: Designed for super-resolution with fine details.
  • Domain: Real-world video.
  • Purpose: Standard benchmark for video super-resolution.
• YouHQ40 (mentioned in Appendix B.4):
  • Source: Not explicitly detailed in the main paper, but likely a high-quality video dataset used for robustness evaluation.
  • Purpose: To assess CreativeVR's robustness to various synthetic input corruptions.

5.2. Evaluation Metrics

For every evaluation metric, the paper provides context, and where formulas are standard, they are supplemented here.

5.2.1. Metrics for AIGC-Artifact Restoration

1. Face Image Quality Assessment (FIQA) Models: These metrics specifically assess the quality of detected faces, which are a common source of artifacts in AIGC. They measure structural plausibility, geometric correctness, and identity preservation. For evaluation, faces are detected by YOLOv8, cropped, and then passed to these models. Relative improvement ($\Delta = \mathrm{score}_{\mathrm{out}} - \mathrm{score}_{\mathrm{in}}$) over input videos is reported.

   • eDifFIQA [5] / DifFIQA [4]:
     • Conceptual Definition: Assess face image quality using denoising diffusion probabilistic models. They measure diffusion-prior robustness, indicating how well a face image aligns with the distribution of high-quality faces learned by a diffusion model. A higher score implies better quality in terms of being "generatable" by a high-quality diffusion prior. eDifFIQA is an efficient version of DifFIQA.
     • Mathematical Formula: These are model-based scores. While the internal mechanism involves complex diffusion models, the output is a scalar quality score. The exact formula for the score itself is usually proprietary to the trained model. Conceptually, it often relates to the likelihood of the image under the diffusion prior or a derived metric from the denoising process.
     • Symbol Explanation: score_out is the FIQA score for the output video's face, score_in is for the input video's face.
   • CLIB-FIQA [33] / CR-FIQA [6]:
     • Conceptual Definition: CLIB-FIQA (Confidence Calibration) and CR-FIQA (Relative Classifiability) assess face quality based on recognition-embedding separability and confidence calibration. They measure how distinctly a face can be recognized and how confident a face recognition system is in that recognition. Higher scores suggest better face quality for downstream recognition tasks.
     • Mathematical Formula: These are typically derived from the outputs of face recognition models. For CR-FIQA, it might involve training a classifier to distinguish between good and bad quality faces based on embeddings. For CLIB-FIQA, it could be related to uncertainty quantification in face recognition.
     • Symbol Explanation: score_out is the FIQA score for the output video's face, score_in is for the input video's face.
   • MR-FIQA [34] / FaceQAN [3]:
     • Conceptual Definition: MR-FIQA (Multi-Reference) and FaceQAN (Adversarial Noise) assess face quality based on multi-reference representations and adversarial noise sensitivity. They probe aspects like texture realism, robustness to perturbations, and how well details match diverse high-quality references.
     • Mathematical Formula: Similar to other FIQA scores, these are typically model-based and output a scalar score.
     • Symbol Explanation: score_out is the FIQA score for the output video's face, score_in is for the input video's face.

2. Aesthetic Score:

   • Conceptual Definition: Measures the overall perceptual appeal or beauty of the image/video clip.
   • Source: A CLIP-LAION initialized model. CLIP (Contrastive Language-Image Pre-training) models learn to associate text and images, making them suitable for evaluating abstract concepts like aesthetics based on learned preferences from large datasets like LAION.
   • Mathematical Formula: Output is a scalar score, typically from a regression model trained on aesthetic ratings.
   • Symbol Explanation: Reported as a score.

3. Objectness Confidence:

   • Conceptual Definition: A proxy for semantic reliability. It measures the confidence of a YOLOv8-based face detector in identifying a face. A higher confidence score suggests a more plausible and well-formed face that is easily recognizable by object detection models.
   • Source: YOLOv8-based face detector.
   • Mathematical Formula: The output confidence score from the YOLOv8 detector.
   • Symbol Explanation: Reported as a score.

4. GPT-based Multi-Aspect Video Quality Assessment:

   • Conceptual Definition: An AI judge (specifically, a GPT-based evaluator) scores videos along six perceptual dimensions on a 0-10 scale. This captures qualitative factors beyond distortion metrics, such as subjective appeal and complex temporal properties.
   • Dimensions: Visual quality, temporal consistency, face quality, motion realism, lighting and atmosphere, and detail preservation.
   • Mathematical Formula: Individual scores are integers from 0-10, averaged to get an overall mean.
   • Symbol Explanation: Each dimension is a score.

5. GPT-based Pairwise Arena-Style Preference Scoring:

   • Conceptual Definition: An AI judge compares two restored clips from the same input sequence and indicates a preference based on temporal consistency and frame quality. This provides a direct perceptual ranking in head-to-head comparisons.
   • Mathematical Formula: Aggregated votes (percentages) for preference.
   • Symbol Explanation: Vote shares.

6. VBENCH Benchmark [18]:

   • Conceptual Definition: A comprehensive benchmark for video generative models that decomposes video quality into multiple human-aligned evaluation dimensions with dedicated automatic metrics.
   • Dimensions & Conceptual Definitions:
     • Subject Consistency: How well the appearance of the main subject remains coherent across frames.
     • Background Consistency: How well the background remains coherent across frames.
     • Motion Smoothness: Quantifies whether motions follow smooth and physically plausible trajectories.
     • Dynamic Degree: Captures the amount of motion present in the video.
     • Aesthetic Quality: Assesses the visual appeal of individual frames.
     • Imaging Quality: Evaluates low-level distortions (e.g., blur, noise, over-exposure) of individual frames.
   • Mathematical Formula: These are automatic metrics, typically scalar scores (e.g., 0-1). The specific formulas are internal to the VBench framework and often involve deep features and learned models.
   • Symbol Explanation: Each dimension is a score.

7. Qwen2.5-VL Object-Centric Quality Assessment:

   • Conceptual Definition: An object-centric evaluation where a large multimodal model (Qwen2.5-VL-72B) acts as a geometry-aware judge on cropped face regions. It scores five detailed aspects related to structural integrity and realism on a 0-10 scale.
   • Dimensions & Conceptual Definitions:
     • Geometry & Silhouette (GE): Judge geometry in the object's semantic frame, assessing plausibility of canonical parts, alignment, straight lines, symmetry. Penalizes distorted contours, wobble, double edges, melted forms.
     • Edge Definition & Continuity (ED): Assess edges along semantic boundaries. Edges must be continuous and coherent. Penalizes fraying, ghost/double edges, halos/ringing.
     • Detail Realism (DT): Rewards fine detail only when it follows the form/material. Penalizes plasticiness, over-smoothing, invented crunchy micro-patterns.
     • Artifact Level (AR; reverse scale): Scores structural artifacts (halos, ringing, tiling, smears, compression artifacts). 10 = no artifacts, 0 = severe artifacts.
     • Tonal Robustness (TN): Considers major tonal failures like blown highlights, crushed shadows, or hue shifts.
   • Mathematical Formula: Integer scores from 0-10, averaged for an aggregate score.
   • Symbol Explanation: Each dimension is a score.

5.2.2. Metrics for Traditional Video Restoration

These are full-reference metrics, meaning they require a clean, ground-truth reference video for comparison.

1. PSNR (Peak Signal-to-Noise Ratio):

   • Conceptual Definition: A common metric for image and video quality that quantifies the ratio between the maximum possible power of a signal and the power of corrupting noise that affects the fidelity of its representation. It is typically expressed in decibels (dB). Higher PSNR values generally indicate better quality (less noise/distortion). It is primarily a distortion-oriented metric.
   • Mathematical Formula: $$\mathrm{PSNR} = 10 \cdot \log_{10} \left( \frac{\mathrm{MAX}_I^2}{\mathrm{MSE}} \right)$$ where $$\mathrm{MSE} = \frac{1}{MN} \sum_{i=0}^{M-1} \sum_{j=0}^{N-1} [I(i,j) - K(i,j)]^2$$
   • Symbol Explanation:
     • $\mathrm{MAX}_I$: The maximum possible pixel value of the image (e.g., 255 for 8-bit images).
     • $\mathrm{MSE}$: Mean Squared Error between the original and the restored image.
     • M, N: Dimensions of the image (height and width).
     • I(i,j): Pixel value at position (i,j) in the original image.
     • K(i,j): Pixel value at position (i,j) in the restored image.
   • Note: For videos, PSNR is typically calculated for each frame and then averaged across all frames.

2. SSIM (Structural Similarity Index Measure) [47]:

   • Conceptual Definition: A perceptual metric that measures the similarity between two images, taking into account luminance, contrast, and structure components. Unlike PSNR, which measures absolute errors, SSIM attempts to model the human visual system's perception of quality. Values range from -1 to 1, with 1 indicating perfect similarity.
   • Mathematical Formula: $$\mathrm{SSIM}(x, y) = \frac{(2\mu_x\mu_y + C_1)(2\sigma_{xy} + C_2)}{(\mu_x^2 + \mu_y^2 + C_1)(\sigma_x^2 + \sigma_y^2 + C_2)}$$
   • Symbol Explanation:
     • x, y: Two image patches (e.g., original and restored).
     • $\mu_x, \mu_y$: Mean intensity of $x$ and $y$.
     • $\sigma_x, \sigma_y$: Standard deviation (contrast) of $x$ and $y$.
     • $\sigma_{xy}$: Covariance of $x$ and $y$ (structural similarity).
     • $C_1 = (K_1L)^2$, $C_2 = (K_2L)^2$: Small constants to avoid division by zero, where $L$ is the dynamic range of pixel values (e.g., 255) and $K_1, K_2 \ll 1$.
   • Note: SSIM is usually calculated on various windows of an image and then averaged. For videos, it's averaged over frames.

3. LPIPS (Learned Perceptual Image Patch Similarity) [57]:

   • Conceptual Definition: A perceptual metric that calculates the distance between two image patches using the activations of a pre-trained deep neural network (e.g., AlexNet, VGG). It correlates better with human judgment of perceptual similarity than traditional metrics like PSNR or SSIM. Lower LPIPS values indicate higher perceptual similarity.
   • Mathematical Formula: $$\mathrm{LPIPS}(x, y) = \sum_l \frac{1}{H_l W_l} \sum_{h,w} \| w_l \odot (\phi_l(x)_{h,w} - \phi_l(y)_{h,w}) \|_2^2$$
   • Symbol Explanation:
     • x, y: Two image patches.
     • $\phi_l$: Feature stack from layer $l$ of a pre-trained network.
     • $w_l$: A learned scaling vector for each channel in layer $l$.
     • $\odot$: Element-wise product.
     • $H_l, W_l$: Height and width of the feature map at layer $l$.
   • Note: For videos, it's calculated per frame and then averaged.

4. DISTS (Deep Image Structure and Texture Similarity) [10]:

   • Conceptual Definition: A perceptual image quality metric that unifies structure and texture similarity, building on deep features. It aims to overcome limitations of traditional metrics by better modeling human perception. Lower DISTS values indicate higher similarity.
   • Mathematical Formula: Similar to LPIPS, DISTS uses features from a pre-trained CNN. The core idea is to compute both structural and textural distances based on these features and then combine them. $$\mathrm{DISTS}(x, y) = \sum_{l=1}^L \left( \alpha_l \cdot d_S(F_l(x), F_l(y)) + \beta_l \cdot d_T(F_l(x), F_l(y)) \right)$$
   • Symbol Explanation:
     • $F_l(x), F_l(y)$: Feature maps of images x, y at layer $l$ of a pre-trained CNN.
     • $d_S$: Structural distance function (e.g., based on normalized cross-covariance).
     • $d_T$: Texture distance function (e.g., based on Gram matrices or feature covariance).
     • $\alpha_l, \beta_l$: Learned weights for structural and texture components at layer $l$.
     • $L$: Total number of layers used.
   • Note: For videos, it's calculated per frame and then averaged.

5. NIQE (Natural Image Quality Evaluator):

   • Conceptual Definition: A no-reference image quality metric (meaning it does not require a ground-truth reference). It measures image quality by comparing the statistical features of a given image to a pre-learned model of "natural" images. Lower NIQE values indicate better quality.
   • Mathematical Formula: NIQE extracts a set of features (e.g., Mean Subtracted Contrast Normalized (MSCN) coefficients) and fits a Multivariate Gaussian (MVG) model to them. The distance between the MVG model of the test image and the MVG model of natural images is computed. $$\mathrm{NIQE}(x) = \sqrt{(\mathbf{v}_1 - \mathbf{v}_2)^T (\Sigma_1 + \Sigma_2)^{-1} (\mathbf{v}_1 - \mathbf{v}_2)}$$
   • Symbol Explanation:
     • $\mathbf{v}_1, \Sigma_1$: Mean vector and covariance matrix of the natural image features.
     • $\mathbf{v}_2, \Sigma_2$: Mean vector and covariance matrix of the test image features.
   • Note: For videos, it's calculated per frame and then averaged.

6. MUSIQ (Multi-scale Image Quality Transformer):

   • Conceptual Definition: A no-reference image quality metric that uses a Transformer-based architecture to predict image quality scores. It operates on multiple scales of the input image, capturing both local details and global contextual information relevant to human perception. Higher MUSIQ scores indicate better quality.
   • Mathematical Formula: MUSIQ is a deep learning model. Its output is a scalar quality score. The internal calculation involves Transformer layers processing multi-scale image features.
   • Symbol Explanation: A scalar quality score.

7. CLIP-IQA (CLIP-based Image Quality Assessment):

   • Conceptual Definition: A no-reference image quality metric that leverages the CLIP model's ability to understand the semantic content of images and align it with text. It assesses image quality by measuring the semantic alignment or "naturalness" in the context of learned image-text relationships. Higher CLIP-IQA scores indicate better quality.
   • Mathematical Formula: CLIP-IQA typically involves using the CLIP image encoder to extract features and then a regression head or similarity score based on these features.
   • Symbol Explanation: A scalar quality score.

8. DOVER (Deep Optimized Video Quality Model):

   • Conceptual Definition: A no-reference video quality metric specifically designed for video. It considers both spatial and temporal aspects of video quality, aiming to predict human perceptual scores for videos. Higher DOVER scores indicate better quality.
   • Mathematical Formula: DOVER is a deep learning model that predicts video quality. The internal calculation involves processing spatio-temporal features.
   • Symbol Explanation: A scalar quality score.

5.3. Baselines

The paper compares CreativeVR against a range of state-of-the-art video restoration and super-resolution methods, representing both traditional and diffusion-based approaches:

• FlashVSR [62]: A diffusion-based streaming video super-resolution method.

• Real-ESRGAN [46]: A Generative Adversarial Network (GAN)-based super-resolution method, specifically designed for real-world blind super-resolution. While primarily an image SR method, it's often applied frame-by-frame to video.

• Real-Viformer [59]: A Transformer-based model investigating attention for real-world video super-resolution.

• ResShift [54]: An efficient diffusion model for image super-resolution using residual shifting. Similar to Real-ESRGAN, an image SR method often applied to video.

• SeedVR2 [41]: A one-step video restoration method via diffusion adversarial post-training.

• STAR [48]: Spatial-temporal augmentation with text-to-video models for real-world video super-resolution.

• Upscale-A-Video [61]: A text-guided latent diffusion model for video upscaling and prompt-driven texture synthesis.

• VEnhancer [13]: A generative space-time enhancement method for video generation, using cross-frame attention.

  These baselines represent a comprehensive selection of strong performers in image/video super-resolution and restoration, including recent diffusion-based and GAN-based approaches, making the comparison robust.

6. Results & Analysis

6.1. Core Results Analysis

CreativeVR demonstrates state-of-the-art performance across various challenging scenarios, particularly excelling in correcting severe structural and temporal artifacts in AIGC and real-world videos, while remaining competitive on standard benchmarks.

6.1.1. Prior-guided Corrective Video Restoration (AIGC Artifacts)

Structural Integrity Evaluation (AIGC54 Benchmark): On the AIGC54 benchmark, CreativeVR achieves the highest gains across all FIQA metrics, indicating superior structural correction capabilities. For instance, it shows up to a +37% relative FIQA improvement over input videos. This is crucial because FIQA metrics are sensitive to geometric correctness, texture realism, and identity preservation. Baselines that are primarily sharpening-oriented often fail to improve face quality, or even regress, as they enhance high-frequency details without fixing warped geometry. CreativeVR's diffusion-prior-guided approach, however, yields consistent positive improvements.

Beyond FIQA, CreativeVR also achieves the highest aesthetic gain and the largest increase in objectness confidence for detected faces. This corroborates that its structural corrections translate to perceptually cleaner and more reliably recognized faces.

The following are the results from Table 1 of the original paper:

Table 1. Structural Integrity Evaluation for AIGC-artifacts. All numbers denote relative improvement $(\%)$ over input videos.

Multi-Aspect Video Quality Assessment (GPT-based): CreativeVR achieves the highest overall mean score in the GPT-based multi-aspect evaluation, demonstrating its ability to deliver high-quality results across various perceptual dimensions. It shows consistent improvements in temporal coherence, visual quality, and detail preservation, confirming the effectiveness of its diffusion priors in correcting structure while maintaining scene semantics. Human evaluations corroborate GPT's rankings.

The following are the results from Table 2 of the original paper:

Table 2. Multi-Aspect Video Quality Assessment for AIGC-artifacts. Higher is better.

VBENCH Benchmark Evaluation (Appendix B.2): CreativeVR achieves the highest overall VBENCH average score (0.8642). It particularly excels in dynamic_degree (0.9444), indicating superior ability to generate videos with stronger and more realistic motion while maintaining temporal coherence. It remains highly competitive in subject_consistency (0.9387) and background_consistency (0.9493), and shows strong performance in imaging_quality and motion_smoothness. Its aesthetic_quality score is also competitive, confirming the visual appeal of the refined videos.

The following are the results from Table 6 of the original paper:

Table 6. Evaluation on VBench Benchmark. Higher is better.

Multi Aspect Scoring Face Quality (Qwen2.5-VL) (Appendix B.3): The Qwen2.5-VL object-centric evaluation further confirms CreativeVR's strong facial quality. It achieves the highest aggregate score (7.84) and outperforms baselines on Geometry & Silhouette, Edge Definition & Continuity, Artifact Level (reverse scale), and Tonal Robustness. This signifies more stable facial shapes, cleaner boundaries, and fewer structural artifacts. While FlashVSR showed slightly higher Detail Realism, CreativeVR maintains competitiveness without introducing spurious high-frequency artifacts.

The following are the results from Table 7 of the original paper:

Table 7. Qwen2.5-VL object-centric evaluation on face crops from AIGC54. Scores are on a 0-10 scale. Our method is superior on four out of five dimensions, corroborating the strong facial quality observed with FaceIQA.

6.1.2. Traditional Video Restoration

On standard benchmarks like UDM10, SPMCS, and REDS30, CreativeVR demonstrates competitive performance. The "Ours (Medium)" setting often matches or surpasses strong SR/VSR baselines for PSNR/SSIM and achieves competitive LPIPS/DISTS scores, particularly for spatial and mild spatio-temporal corruptions. This indicates that a model optimized for complex AIGC artifacts can generalize well to classical degradations without specific task-tuning. The ability to switch between "Medium" (for distortion-oriented metrics) and "Strong" (for generative artifacts) highlights the flexibility of the approach.

The following are the results from Table 3 of the original paper:

Table 3. Reference-based benchmarks across various datasets. $\uparrow$ higher is better, $\downarrow$ lower is better for LPIPS and DISTS.

6.1.3. Robustness to Input Degradations (Appendix B.4)

CreativeVR demonstrates robust performance across varying levels of synthetic input corruptions (spatial downsampling, spatio-temporal downsampling, spatio-temporal Light, and spatio-temporal Strong) on REDS30 and YouHQ40 benchmarks. The "Medium" setting consistently matches or surpasses baselines for spatial and mild spatio-temporal corruptions. Even the "Strong" setting remains competitive under the most severe ST-Strong case, further highlighting the model's ability to handle diverse degradations without task-specific tuning. This confirms that the single refiner can robustly undo both synthetic spatial degradations and harder spatio-temporal artifacts without sacrificing perceptual quality.

The following are the results from Table 8 of the original paper:

Table 8. Robustness to input degradations on REDS30. $\uparrow$ higher is better, $\downarrow$ lower is better for LPIPS, DISTS, and NIQE.

The following are the results from Table 9 of the original paper:

Table 9. Robustness to input degradations on YouHQ40. $\uparrow$ higher is better, $\downarrow$ lower is better for LPIPS, DISTS, and NIQE.

6.2. Ablation Studies / Parameter Analysis

6.2.1. Synthetic Degradation Strength During Training

The paper ablates the impact of different degradation strength levels (Light, Medium, Strong) used during the training of the temporally coherent degradation module. All variants use the same backbone and training recipe, with only the corruption schedule (blur, warping, morphing, frame dropping, temporal downsampling) varied in intensity.

The following are the results from Table 4 of the original paper:

Table 4. FIQA gains vs. training strength. Values are relative improvements $(\%)$ over the input videos.

Analysis:

• Strong degradation yields the largest structure gains across all FIQA metrics on the AIGC54 benchmark, indicating that training with more severe and realistic structural failures explicitly teaches the model to correct them.

• Light offers only mild sharpening, while Medium provides moderate improvements. This demonstrates the effectiveness of the temporally coherent degradation module in targeting complex artifacts.

• GPT-based pairwise preferences (Table 5 below) show the same trend, with Strong augmentations leading to higher perceived quality. Qualitatively, as shown in Figure 4, Strong best removes geometric distortions without altering pose or identity.

  The following figure (Figure 4 from the original paper) shows the effect of degradation strength:

  VLM Description: The image is an illustration showing the effects of different degradation strengths. The leftmost side shows the input frame, while the four images on the right display the zoomed input and the outputs enhanced with light, medium, and strong augmentation, highlighting the significant improvements in faces and details.

The following are the results from Table 5 of the original paper:

Table 5. Preference scores (1-10) vs. training strength.

Trade-off on Distortion-Oriented Metrics: On distortion-oriented SR/VR metrics (e.g., PSNR, SSIM on SPMCS and REDS30), Medium slightly outperforms Strong. This is attributed to the fact that heavy synthetic degradations (used in Strong) might induce prior-driven corrections that deviate from pixel-wise ground truth, even if they result in perceptually better outputs. Consequently, the paper recommends "Medium" for distortion benchmarks and "Strong" as the default for AIGC/real-world refinement where perceptual quality and artifact correction are paramount.

6.2.2. Inference Control Scale ($\gamma_{\ell}$)

At inference time, the model exposes a single control scale $s$ that rescales all adapter gains $\gamma_{\ell}$. This knob modulates how strongly the degraded video guides the frozen prior.

As can be seen from the results in Figure 5:

VLM Description: The image is a chart showing the input image under different precision settings. The first column displays the zoomed input, and the subsequent columns show the restoration results at various precision knob settings (0.1, 0.2, 0.4, 0.6, 1.0), clearly illustrating the balance between detail preservation and corrective synthesis.

Analysis:

• Higher $s$ values (e.g., 1.0): Yield more precise, high-fidelity restoration that closely adheres to the input. This is suitable for minor degradations where exact reconstruction is desired.

• Lower $s$ values (e.g., 0.1, 0.2): Allow for more prior-driven, highly creative re-synthesis. This enables stronger structure- and motion-corrective behavior on challenging content, where the model's generative prior can actively repair artifacts rather than just refine existing pixels.

  This precision knob offers users granular control over the trade-off, making CreativeVR highly adaptable to different restoration needs.

6.2.3. Diverse Applications (Appendix C.3)

Beyond its primary restoration task, CreativeVR demonstrates versatility in other applications by adjusting the inference scale $\gamma_{\ell}$:

• CG-to-real translation: At lower values of the inference scale $\gamma_{\ell}$, CreativeVR can transform stylized computer-generated (CG) footage into more realistic renderings. It preserves the identity of the CG character and the overall scene layout while enhancing textures, lighting, and shading to appear more photorealistic.

  As can be seen from the results in Figure 10:

  VLM Description: The image is a schematic representation of six consecutive frames showing a girl in a yellow sweater sitting up and cheering on a bed. This display highlights potential structural and temporal consistency issues in the motion sequence, which are relevant to the research theme of CreativeVR.

• Slow-motion generation: CreativeVR can synthesize slow-motion, high-frame-rate versions of existing videos. This is achieved by first upsampling the input sequence temporally via simple linear interpolation (which introduces artifacts like morphing and blur in intermediate frames). Passing this interpolated clip through the CreativeVR refiner then yields temporally smooth, detail-preserving frames, effectively creating a visually coherent slow-motion video.

  As can be seen from the results in Figure 11:

  VLM Description: The image is an illustration showing the reactions of spectators watching an event, including various postures and expressions, emphasizing the dynamics and social interactions in the scene.

These additional applications highlight the inherent generative capabilities and adaptability of CreativeVR's diffusion-prior-guided approach.

7. Conclusion & Reflections

7.1. Conclusion Summary

CreativeVR introduces a pioneering diffusion-prior-guided framework for video restoration that effectively addresses severe structural and temporal artifacts in both AI-generated content (AIGC) and real-world videos. By leveraging a frozen Text-to-Video Diffusion Transformer (T2V DiT) backbone augmented with lightweight deep adapters, the method provides a unified solution for artifact correction. A key innovation is the temporally coherent degradation module used during training, which synthesizes realistic structural failures, enabling the model to explicitly learn to repair complex artifacts rather than merely preserving them. The framework's controllable precision knob ($\gamma_{\ell}$) offers a critical advantage, allowing users to smoothly trade off between fidelity-oriented restoration and more aggressive, prior-guided structural correction, making it highly versatile. Evaluated against a novel AIGC54 benchmark and standard VR datasets, CreativeVR demonstrates state-of-the-art performance, achieving significant improvements in face quality, perceptual appeal, and temporal consistency, while also maintaining competitive results on traditional metrics and operating at practical throughputs (13 FPS at 720p) with strong zero-shot scalability.

7.2. Limitations & Future Work

The paper does not explicitly dedicate a section to "Limitations" or "Future Work." However, some implicit observations can be made:

• Trade-off between Pixel-wise Fidelity and Perceptual Quality: The ablation study on degradation strength (Table 3 vs. Tables 4 and 5) reveals that while stronger augmentations lead to better perceptual and structural artifact correction (higher FIQA, GPT scores), they can sometimes slightly reduce PSNR/SSIM on traditional benchmarks, which are pixel-wise distortion metrics. This suggests that the prior-driven corrections might deviate from the exact ground truth pixels in favor of perceptually plausible syntheses. This is a common characteristic of generative models and implies a nuanced choice depending on the specific application's priority.
• Generalization to Extremely Novel Artifacts: While the temporally coherent degradation module is designed to mimic realistic failures, there might be highly unusual or out-of-distribution artifacts not covered by this synthetic curriculum. The model's performance on such unforeseen degradations is not explicitly discussed.
• Computational Cost for Very High Resolutions/Long Videos: Although the paper reports practical throughput for 720p and 1080p and zero-shot scalability, the underlying DiT backbone and VAE still involve significant computational resources. For extremely high resolutions (e.g., 4K, 8K) or very long video sequences, computational demands could still be a factor, even with the efficient adapter approach.
• Specific Control over Artifact Types: The single precision knob provides a global control over prior strength. Future work could explore more granular control mechanisms, allowing users to target specific artifact types (e.g., "fix faces only," "stabilize background wobble") rather than a general corrective behavior.

7.3. Personal Insights & Critique

CreativeVR presents a highly impactful solution to a critical and growing problem: the pervasive artifacts in both generative AI outputs and degraded real-world videos. The paper's strength lies in its pragmatic approach to leveraging the power of large T2V diffusion models without the prohibitive cost of full fine-tuning.

• Novelty of Degradation Module: The temporally coherent degradation module is a particularly insightful contribution. Instead of generic noise, tailoring the training degradations to mimic the structural and temporal failure modes of AIGC is a clever way to align the model's generative prior with the specific task of artifact correction. This targeted curriculum likely accounts for the significant gains observed in FIQA and GPT-based metrics.

• Effectiveness of Deep Adapters: The deep adapter approach, combined with a frozen T2V DiT backbone, is an elegant solution for efficient knowledge transfer. It allows CreativeVR to benefit from the rich generative priors learned by massive T2V models while keeping the trainable parameters minimal, thus achieving high throughput and scalability.

• User Control with Precision Knob: The precision knob is a crucial user-facing feature. It addresses a common challenge in generative restoration—the balance between fidelity to the input and the model's ability to "hallucinate" corrections. Providing explicit control over this trade-off makes the model much more versatile and practical for various applications where different levels of "creativity" or "faithfulness" might be required.

• Comprehensive Evaluation Protocol: The introduction of the AIGC54 benchmark and the multi-faceted evaluation strategy (combining FIQA, GPT-based metrics, and traditional metrics) is commendable. It recognizes that traditional PSNR/SSIM are insufficient for evaluating corrections of semantic artifacts and provides a more holistic view of quality.

• Practical Value: The reported throughput and zero-shot generalization capabilities make CreativeVR highly practical for integration into creative pipelines, where real-time or near real-time processing of high-resolution video is essential. Its ability to serve as a lightweight enhancement layer for existing T2V backbones further adds to its appeal for production environments.

  In terms of critique, as noted above, the paper could have explicitly discussed limitations more. While CreativeVR addresses a wide range of artifacts, the precision knob offers a global control. Future research might investigate hierarchical or localized control, allowing users to specify the degree of correction for different regions or types of artifacts within a video. Nevertheless, CreativeVR stands out as a robust and innovative solution, offering a clear path toward enhancing the quality and usability of both AI-generated and real-world video content. Its methods and insights are highly transferable to other domains involving generative model outputs that suffer from structural imperfections, making it a valuable contribution to the field of generative AI and multimedia processing.