Compute-Optimal Scaling Laws for Human Motion Generation Released
andrew_n_carr · x · 2026-08-25
This paper presents compute-optimal scaling laws for human motion generation, addressing the historical data constraints in the field. By building the world's largest, high-quality human-motion dataset and training hundreds of models, the authors prove that scaling significantly improves performance. This holds promise for animators, roboticists, and game developers.
Related event: Human Motion Generation Follows Chinchilla Scaling Laws, Study Finds(3 posts)→
More from Research
- Models lack independent research capabilities; RSI predictions seem overly optimistic — BlancheMinerva · 2026-08-25
- GLiNER 2.5 Launches with Architecture Upgrade for Long-Context Extraction — huggingface · 2026-08-25
- Analysis confirms stealth/ox-alpha is a Z.ai GLM model — PawelHuryn · 2026-08-25
- ProteinDPO aligns protein models for stability, published in Nature Methods — BrianHie · 2026-08-25
- Thinking Machines proposes a safe path for open-weight model releases — luke_drago_ · 2026-08-25
- Headlong experiments with persistent agency via exponential backoff — lateinteraction · 2026-08-25