In this work, we present an automatic human-to-quadruped puppeteering framework that produces plausible and controllable quadruped motions from ordinary human motion data.
July 30, 2026
arXiv (2026)
Authors
Fatemeh Zargarbashi (DisneyResearch|Studios/ETH Zurich)
Zehong Qiu (DisneyResearch|Studios) Dhruv Agrawal (DisneyResearch|Studios/ETH Zurich)
Stelian Coros (ETH Zurich) Robert W. Sumner (DisneyResearch|Studios/ETH Zurich)
Martin Guay (DisneyResearch|Studios)
Jakob Buhmann (DisneyResearch|Studios)
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 strategy, our method supports a wide range of actions, including walking, running, jumping, sitting, and lying. Furthermore, we enable fine-grained intuitive control of the quadruped motion such as head movement control and individual limb puppeteering. Experimental results demonstrate improved motion realism and controllability compared to existing retargeting approaches, highlighting the effectiveness of our framework as a tool for animation and virtual production applications.