Arxiv paper - EquiVDM: Equivariant Video Diffusion Models with Temporally Consistent Noise

In this episode, we discuss EquiVDM: Equivariant Video Diffusion Models with Temporally Consistent Noise by The authors of the paper are: - **Chao Liu** - **Arash Vahdat**. The paper presents a video diffusion framework that utilizes temporally consistent noise to generate coherent and high-quality video frames without needing specialized modules. By ensuring the model handles spatial transformations consistently, it effectively captures and aligns motion patterns from input videos and maintains 3D consistency when extended to 3D meshes. Experimental results show that this method outperforms current state-of-the-art approaches in motion alignment, 3D consistency, video quality, and efficiency.