Two2Four: Generative Quadruped Puppeteering from Human Motion

Researchers from DisneyResearch|Studios and ETH Zurich have developed Two2Four, a generative framework that automatically converts ordinary human motion into plausible and controllable quadruped animations, using a two-stage diffusion model trained on quadruped motion data. The system supports actions such as walking, running, jumping, sitting, and lying, and offers fine-grained control like head movement and individual limb puppeteering, outperforming existing retargeting methods in realism and controllability for animation and virtual production.

Two2Four: Generative Quadruped Puppeteering from Human Motion 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.