magic-animate

Why we included this project

MagicAnimate takes a single reference image of a person and a short driving video, then generates a new clip where that person performs the driving motion while keeping their identity and appearance intact. Published at CVPR 2024, it is one of the more prominent open-source diffusion models for human image animation, and its main strength is temporal consistency: the output stays free of the flicker and identity drift that frame-by-frame approaches tend to introduce. The repo ships complete inference scripts and a Gradio demo that runs either locally or on a hosted Space, so you can get from setup to an animated result without writing pipeline code yourself. One practical caveat: you will need a GPU and a decent amount of storage, because it depends on pretrained Stable Diffusion 1.5 and VAE checkpoints in addition to the MagicAnimate weights. If believable human motion transfer is the goal rather than general text-to-video, this is a solid reference implementation to start from.