Two2Four: Generative Quadruped Puppeteering from Human Motion
Realistic animal motion for virtual production is typically obtained either through motion capture of highly trained performers who accurately mimic animal behavior, or by retargeting ordinary human motion using complex control setups. Both approaches are challenging and often fail to fully reproduce the nuances of natural animal motion, motivating data-driven alternatives. We present an automatic human-to-quadruped puppeteering framework that produces plausible and controllable quadruped motions from ordinary human motion data. Our approach employs a two-stage generative diffusion model trained purely on quadruped motion data. By introducing a structured conditioning and inpainting strat...
| Authors | Fatemeh Zargarbashi, Zehong Qiu, Dhruv Agrawal et al. |
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| Primary category | cs.GR |
| Published | 28 July 2026 |
| Last updated at source | 28 July 2026 |
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