Chinchilla-style scaling laws found for human motion: the fifth scalable modality

andrew_n_carr · x · 2026-08-25

A team presents their flagship paper, "Compute-Optimal Scaling Laws for Human Motion Generation."

Background: Human motion generation has stayed niche and hard to scale — animators, roboticists, and game devs see the potential, but production rigor has lagged. The authors argue the root cause is severe data constraints, with no prior proof that scaling helps.
Method: They built the world's largest high-quality human-motion dataset and trained hundreds of models across scales.
Finding: Compute-optimal scaling for motion matches the Chinchilla scaling found in language, making motion the fifth data modality we know how to scale, after text, images, audio, and video.

More from Research