Progressive Autoregressive Video Diffusion Models

Understand

Current frontier video diffusion models have demonstrated remarkable results at generating high-quality videos.

• However, they can only generate short video clips, normally around 10 seconds or 240 frames, due to computation limitations during training.
• Existing methods naively achieve autoregressive long video generation by directly placing the ending of the previous clip at the front of the attention window as conditioning, which leads to abrupt scene changes, unnatural motion, and error accumulation.
• In this work, we introduce a more natural formulation of autoregressive long video generation by revisiting the noise level assumption in video diffusion models.