Fine-gained Zero-shot Video Sampling
Dengsheng Chen, Jie Hu, Xiaoming Wei, Enhua Wu
2024-08-01
Summary
This paper introduces a new method called Fine-grained Zero-shot Video Sampling (ZS^2) that allows for creating high-quality video clips from existing image models without needing extra training. It aims to improve how videos are generated by leveraging techniques used in image synthesis.
What's the problem?
Many current methods for generating videos from images are complicated and require large amounts of data and processing power. They often struggle to create longer or more complex videos, leading to short clips with simple movements that don't capture detailed actions or changes. This limits their usefulness in applications that need more dynamic and realistic video content.
What's the solution?
The authors propose the ZS^2 algorithm, which can directly sample video clips from image synthesis methods like Stable Diffusion without any additional training. ZS^2 uses advanced techniques such as a dependency noise model and temporal momentum attention to ensure that the generated videos are consistent and coherent. This allows it to create videos that not only look good but also maintain smooth motion and realistic actions, even when generating new content that hasn't been seen before.
Why it matters?
This research is significant because it enhances the ability to generate videos quickly and efficiently, making it easier for creators to produce high-quality content without the need for extensive resources. By achieving state-of-the-art performance in zero-shot video generation, ZS^2 opens up new possibilities for applications in film, gaming, and virtual reality, where dynamic and engaging video content is essential.
Abstract
Incorporating a temporal dimension into pretrained image diffusion models for video generation is a prevalent approach. However, this method is computationally demanding and necessitates large-scale video datasets. More critically, the heterogeneity between image and video datasets often results in catastrophic forgetting of the image expertise. Recent attempts to directly extract video snippets from image diffusion models have somewhat mitigated these problems. Nevertheless, these methods can only generate brief video clips with simple movements and fail to capture fine-grained motion or non-grid deformation. In this paper, we propose a novel Zero-Shot video Sampling algorithm, denoted as ZS^2, capable of directly sampling high-quality video clips from existing image synthesis methods, such as Stable Diffusion, without any training or optimization. Specifically, ZS^2 utilizes the dependency noise model and temporal momentum attention to ensure content consistency and animation coherence, respectively. This ability enables it to excel in related tasks, such as conditional and context-specialized video generation and instruction-guided video editing. Experimental results demonstrate that ZS^2 achieves state-of-the-art performance in zero-shot video generation, occasionally outperforming recent supervised methods. Homepage: https://densechen.github.io/zss/.