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.
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