Open-AnimateAnyone

Why we included this project

Open-AnimateAnyone is a working reimplementation of the Animate Anyone method, and unlike many reimplementations it ships training code alongside the usual inference setup. The pipeline builds on MagicAnimate and AnimateDiff, so anyone familiar with those diffusion frameworks can pick it up quickly. It takes a single reference photo of a person and a pose sequence, then generates a video of that person following the poses. The author is upfront that the model was trained only on small datasets like TikTok and UBC, so output quality falls short of the official paper unless you bring your own larger, cleaner data. That honesty makes the README a genuinely useful training guide: it walks through the two-stage procedure and explains which design choices, such as enlarging the poseguider output channels or keeping the unet unfrozen, matter when data is scarce. For teams that want to experiment with pose-driven human video generation on their own hardware, this is a solid starting point.