Adaptive Video Distillation: Mitigating Oversaturation and Temporal Collapse in Few-Step Generation
Proposes Adaptive Video Distillation with adaptive regression and temporal regularization, enabling stable few-step high-quality video synthesis.
Key Findings
Methodology
This paper introduces a distillation framework combining adaptive regression loss and temporal regularization to optimize video diffusion models for few-step generation. The adaptive regression loss dynamically weights spatial supervision to prevent oversaturation, while the temporal regularization enforces frame-to-frame coherence, mitigating temporal collapse. Additionally, an inference-time frame interpolation strategy reduces computational load by performing inference at lower frame rates during high-noise steps and interpolating in latent space. Extensive experiments on VBench and VBench2 demonstrate that this approach achieves stable, high-fidelity video synthesis with fewer steps, outperforming existing baselines in perceptual quality and motion realism.
Key Results
- On VBench2, our method achieves a total quality score of 80.13 in 4 steps, surpassing DMD (61.10) and rCM (61.55) by over 20%, demonstrating significant efficiency and quality gains. In VBench1, the score reaches 82.57, indicating robust performance across benchmarks. Ablation studies show that adaptive regression and temporal regularization each contribute approximately 10% improvements, while frame interpolation reduces inference time by nearly 50%.
- Across multiple metrics, our approach excels in motion naturalness, color saturation control, and detail preservation, especially in long sequences where temporal mode collapse is minimized. The model generates realistic videos with fine spatial details, natural colors, and coherent motion, even with fewer than five denoising steps, making it suitable for real-time applications.
- Analysis confirms that dynamic weighting of supervision prevents oversaturation, and temporal regularization enhances motion diversity. Frame interpolation effectively shortens inference sequences without quality loss, collectively advancing the practicality of few-step video diffusion. These innovations lead to more stable, efficient, and high-quality video synthesis.
Significance
This work addresses the critical bottleneck of high inference cost in video diffusion models, enabling real-time, high-fidelity video generation. By integrating adaptive supervision and temporal regularization, it effectively overcomes issues like temporal inconsistency and mode collapse, paving the way for applications in entertainment, virtual reality, and content creation. The proposed framework offers a scalable solution for deploying diffusion-based video synthesis in industry, with potential for further extension to multi-modal and multi-task scenarios, significantly impacting both academia and industry.
Technical Contribution
The core technical contribution lies in the novel combination of adaptive regression loss and temporal regularization within a distillation framework tailored for video diffusion models. The adaptive loss dynamically adjusts supervision weights based on real-time deviation estimates, preventing oversaturation. The temporal regularization enforces motion diversity by penalizing low-variance sequences, ensuring temporal coherence. The inference-time frame interpolation reduces computational complexity by leveraging the correlation between high-noise denoising and inter-frame similarity. These innovations collectively enable stable, few-step high-quality video synthesis, representing a significant advancement over prior methods that either require many steps or suffer from artifacts.
Novelty
This is the first work to systematically incorporate adaptive supervision and temporal regularization into a distillation framework specifically designed for video diffusion models. Unlike previous image-focused methods, this approach explicitly targets temporal consistency and motion diversity, introducing a decoupled inference strategy that significantly reduces sampling steps without sacrificing quality. Its novel combination of dynamic weighting, temporal regularization, and frame interpolation sets it apart from existing techniques, offering a comprehensive solution to longstanding challenges in few-step video generation.
Limitations
- Despite improvements, the model may still struggle with highly complex or fast-moving scenes, where motion artifacts or detail loss occur due to limited training data or model capacity.
- Frame interpolation, while reducing computation, can introduce minor artifacts or temporal inconsistencies in highly dynamic scenes, affecting visual coherence.
- The method relies on large-scale high-quality datasets and pretrained models, which may limit accessibility and scalability in resource-constrained environments.
Future Work
Future research will explore multi-scale regularization to better handle complex motions, incorporate unsupervised learning to reduce data dependency, and optimize frame interpolation to eliminate artifacts. Extending the framework to multi-modal inputs (audio, text) and real-time interactive applications will further broaden its impact, aiming for seamless integration into entertainment, VR, and live content creation.
AI Executive Summary
Video synthesis has become a pivotal challenge in artificial intelligence, with diffusion models leading recent advances due to their high fidelity and diversity. However, their high computational demands hinder practical deployment, especially in real-time scenarios. Traditional distillation methods like Distribution Matching Distillation (DMD) have improved efficiency but often cause issues such as oversaturation and temporal inconsistency, which degrade visual quality over long sequences.
To address these challenges, this work introduces an innovative distillation framework that integrates adaptive regression loss and temporal regularization. The adaptive regression loss dynamically weights supervision signals, preventing the model from overemphasizing local details that lead to oversaturation. Meanwhile, temporal regularization enforces frame-to-frame coherence, effectively mitigating temporal mode collapse and ensuring natural motion. An additional inference-time frame interpolation strategy reduces the number of denoising steps by performing low-frame-rate inference during high-noise stages and interpolating in latent space, significantly lowering computational costs.
Extensive experiments on the VBench and VBench2 benchmarks demonstrate that the proposed method achieves stable, high-quality video generation with as few as four denoising steps. The results show a total quality score exceeding 80, outperforming existing methods by a large margin. The ablation studies confirm that each component—adaptive supervision, temporal regularization, and frame interpolation—contributes substantially to performance gains. This framework not only accelerates inference but also enhances the perceptual fidelity and motion realism of generated videos, making it suitable for real-time applications.
Despite these advances, some limitations remain, such as potential artifacts in highly dynamic scenes and reliance on large datasets. Future work will focus on multi-scale regularization, unsupervised training, and broader modality integration. Overall, this research marks a significant step toward practical, high-fidelity, few-step video synthesis, opening new avenues for immersive content creation and interactive media.
Deep Dive
Abstract
Video generation has recently emerged as a central task in the field of generative AI. However, the substantial computational cost inherent in video synthesis makes model distillation a critical technique for efficient deployment. Despite its significance, there is a scarcity of methods specifically designed for video diffusion models. Prevailing approaches often directly adapt image distillation techniques, which frequently lead to artifacts such as oversaturation, temporal inconsistency, and mode collapse. To address these challenges, we propose a novel distillation framework tailored specifically for video diffusion models. Its core innovations include: (1) an adaptive regression loss that dynamically adjusts spatial supervision weights to prevent artifacts arising from excessive distribution shifts; (2) a temporal regularization loss to counteract temporal collapse, promoting smooth and physically plausible sampling trajectories; and (3) an inference-time frame interpolation strategy that reduces sampling overhead while preserving perceptual quality. Extensive experiments and ablation studies on the VBench and VBench2 benchmarks demonstrate that our method achieves stable few-step video synthesis, significantly enhancing perceptual fidelity and motion realism. It consistently outperforms existing distillation baselines across multiple metrics.