Make Pixels Dance: High-Dynamic Video Generation
Yan Zeng Guoqiang Wei Jiani Zheng Jiaxin Zou Yang Wei Yuchen Zhang Hang Li
Creating high-dynamic videos such as motion-rich actions and sophisticated
visual effects poses a significant challenge in the field of artificial
intelligence. Unfortunately, current state-of-the-art video generation methods,
primarily focusing on text-to-video generation, tend to produce video clips
with minimal motions despite maintaining high fidelity. We argue that relying
solely on text instructions is insufficient and suboptimal for video
generation. In this paper, we introduce PixelDance, a novel approach based on
diffusion models that incorporates image instructions for both the first and
last frames in conjunction with text instructions for video generation.
Comprehensive experimental results demonstrate that PixelDance trained with
public data exhibits significantly better proficiency in synthesizing videos
with complex scenes and intricate motions, setting a new standard for video
generation.