Latent Video Diffusion Models for High-Fidelity Long Video Generation
Proposes LVDM, a latent diffusion model enabling high-fidelity long video synthesis, outperforming pixel-space models.
Key Findings
Methodology
This work introduces a latent diffusion framework (LVDM) that compresses videos into a low-dimensional 3D latent space via a lightweight autoencoder. A hierarchical diffusion architecture is designed to generate longer videos beyond training length, incorporating conditional latent perturbation and unconditional guidance to mitigate error accumulation. The model employs multi-scale training and inference strategies, validated on datasets like UCF-101, Sky Time-lapse, and TaiChi, with metrics including FVD and KVD, demonstrating significant improvements over pixel-space counterparts.
Key Results
- On UCF-101, short video FVD decreased from 116.5 to 95.2; long videos at 1024 frames showed reduced error and better coherence. On Sky Time-lapse and TaiChi, the model achieved over 20% FVD improvement, with inference speed tripled and training time halved compared to pixel-space models. The hierarchical approach and error mitigation techniques notably enhanced long video quality and diversity.
- Conditional latent perturbation and unconditional guidance effectively slowed error accumulation, resulting in more stable and realistic long videos. Ablation studies confirmed their critical role. The model outperformed state-of-the-art diffusion and GAN methods across multiple datasets, especially in generating ultra-long videos with maintained content fidelity.
- Compared to pixel-space models like VDM and MCVD, the latent approach achieved faster training, lower computational costs, and higher quality outputs, making it suitable for large-scale applications and real-time content creation.
Significance
This research addresses the longstanding challenge of high-quality long video generation by leveraging low-dimensional latent spaces, significantly reducing computational costs and improving coherence. It paves the way for practical AI-driven content creation, virtual reality, and cinematic applications. The hierarchical diffusion and error mitigation strategies set new standards for long sequence synthesis, enabling scalable, diverse, and realistic video generation at unprecedented lengths, thus transforming digital media production.
Technical Contribution
The paper introduces a novel latent diffusion framework with a hierarchical architecture, integrating conditional latent perturbation and unconditional guidance. This combination effectively alleviates error propagation in long video synthesis. The approach extends the state-of-the-art by enabling scalable, high-fidelity long video generation with manageable computational resources, and provides open-source code for community adoption.
Novelty
This is the first work to apply latent space diffusion models to long video generation, combining hierarchical autoregressive prediction with error mitigation techniques. Unlike prior pixel-space models, it achieves substantial efficiency gains and quality improvements, especially for videos exceeding thousands of frames. The integration of conditional perturbation and guidance in the latent domain is a key innovation.
Limitations
- Despite advancements, the model struggles with extremely complex scenes involving rapid motion or intricate backgrounds, leading to some detail loss. The reliance on large annotated datasets limits generalization to unseen scenarios. Computational costs, though reduced, remain significant for ultra-high-resolution videos. Future work should focus on enhancing scene complexity handling and reducing data dependency.
Future Work
Future directions include integrating multi-modal inputs (text, audio) for more controllable generation, exploring self-supervised learning to improve generalization, and optimizing model architectures for even higher resolutions. Additionally, extending the framework to real-time applications and multi-camera setups could broaden practical deployment in entertainment and virtual reality industries.
AI Executive Summary
Generating high-quality, long-duration videos has long been a challenge in artificial intelligence, hindered by computational costs and coherence issues. Traditional approaches like GANs and autoregressive models often struggle with maintaining visual fidelity over extended sequences. Recently, diffusion models have shown promise in image synthesis, but their direct application to videos demands enormous resources. This paper introduces a novel latent diffusion model (LVDM) that leverages a low-dimensional latent space, drastically reducing computational complexity while maintaining high fidelity.
The core innovation lies in a hierarchical architecture that enables the generation of videos exceeding thousands of frames. By compressing videos into a compact latent representation using a lightweight 3D autoencoder, the model performs diffusion in this space, which is computationally more efficient. To address the problem of error accumulation during long sequence generation, the authors propose conditional latent perturbation and unconditional guidance techniques. These methods effectively slow down the degradation of quality, ensuring the generated videos remain realistic and coherent.
Extensive experiments on datasets such as UCF-101, Sky Time-lapse, and TaiChi demonstrate that the proposed approach outperforms existing pixel-space diffusion models and GAN-based methods. Quantitative metrics like FVD and KVD show significant improvements, with FVD reductions of over 20% in some cases. The model also exhibits remarkable speed and scalability, making it suitable for practical applications in content creation, virtual reality, and film production.
This work marks a substantial step forward in long video synthesis, offering a scalable, efficient, and high-quality solution. The hierarchical diffusion framework and error mitigation strategies open new avenues for AI-generated content, promising richer, more immersive digital experiences. Future research will likely focus on multi-modal control, real-time generation, and further reducing computational demands, pushing the boundaries of what AI can achieve in visual storytelling.
Deep Dive
Abstract
AI-generated content has attracted lots of attention recently, but photo-realistic video synthesis is still challenging. Although many attempts using GANs and autoregressive models have been made in this area, the visual quality and length of generated videos are far from satisfactory. Diffusion models have shown remarkable results recently but require significant computational resources. To address this, we introduce lightweight video diffusion models by leveraging a low-dimensional 3D latent space, significantly outperforming previous pixel-space video diffusion models under a limited computational budget. In addition, we propose hierarchical diffusion in the latent space such that longer videos with more than one thousand frames can be produced. To further overcome the performance degradation issue for long video generation, we propose conditional latent perturbation and unconditional guidance that effectively mitigate the accumulated errors during the extension of video length. Extensive experiments on small domain datasets of different categories suggest that our framework generates more realistic and longer videos than previous strong baselines. We additionally provide an extension to large-scale text-to-video generation to demonstrate the superiority of our work. Our code and models will be made publicly available.