Paper page - OmniHumanoid: Streaming Cross-Embodiment Video Generation with Paired-Free Adaptation

Source: https://huggingface.co/papers/2605.12038

Abstract

OmniHumanoid enables cross-embodiment video generation by factorizing motion transfer and embodiment-specific adaptation, allowing scalable adaptation to new humanoid embodiments using unpaired data.

Cross-embodiment video generationaims to transfer motions across differenthumanoid embodiments, such as human-to-robot and robot-to-robot, enabling scalable data generation for embodied intelligence. A major challenge in this setting is that motion dynamics are partly transferable across embodiments, whereas appearance and morphology remain embodiment-specific. Existing approaches often entangle these factors, and many require paired data for every target embodiment, which limits scalability to new robots. We present OmniHumanoid, a framework that factorizes transferable motion learning andembodiment-specific adaptation. Our method learns a sharedmotion transfermodel frommotion-aligned paired videosspanning multiple embodiments, while adapting to a new embodiment using only unpaired videos through lightweight embodiment-specific adapters. To reduce interference betweenmotion transferand embodiment adaptation, we further introduce abranch-isolated attentiondesign that separates motion conditioning from embodiment-specific modulation. In addition, we construct asynthetic cross-embodiment datasetwithmotion-aligned paired videosrendered across diverse humanoid assets, scenes, and viewpoints. Experiments on both synthetic and real-world benchmarks show that OmniHumanoid achieves strongmotion fidelityandembodiment consistency, while enabling scalable adaptation to unseenhumanoid embodimentswithout retraining the shared motion model.

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