OnlineVPO: Align Video Diffusion Model with Online Video-Centric Preference Optimization

Understand

Video diffusion models (VDMs) have demonstrated remarkable capabilities in text-to-video (T2V) generation.

• Despite their success, VDMs still suffer from degraded image quality and flickering artifacts.
• To address these issues, some approaches have introduced preference learning to exploit human feedback to enhance the video generation.
• However, these methods primarily adopt the routine in the image domain without an in-depth investigation into video-specific preference optimization.