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

2026-05-12

AI Summary

This work addresses the limited generalizability of existing cross-embodiment video generation methods, which suffer from entangled motion and morphology representations and rely on paired data for target embodiments. To overcome these limitations, the authors propose a motion-morphology disentangled modeling framework that enables rapid adaptation to new robots without requiring paired data, leveraging a shared motion model and lightweight embodiment adapters. A novel branch-isolated attention mechanism is introduced to effectively separate motion conditioning from embodiment-specific modulation. The study also presents the first large-scale synthetic dataset of cross-embodiment paired videos. Experimental results demonstrate high motion fidelity and embodiment consistency on both synthetic and real-world benchmarks, with successful zero-shot transfer to unseen humanoid embodiments without retraining the shared motion model.

Abstract

Cross-embodiment video generation aims to transfer motions across different humanoid 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 and embodiment-specific adaptation. Our method learns a shared motion transfer model from motion-aligned paired videos spanning multiple embodiments, while adapting to a new embodiment using only unpaired videos through lightweight embodiment-specific adapters. To reduce interference between motion transfer and embodiment adaptation, we further introduce a branch-isolated attention design that separates motion conditioning from embodiment-specific modulation. In addition, we construct a synthetic cross-embodiment dataset with motion-aligned paired videos rendered across diverse humanoid assets, scenes, and viewpoints. Experiments on both synthetic and real-world benchmarks show that OmniHumanoid achieves strong motion fidelity and embodiment consistency, while enabling scalable adaptation to unseen humanoid embodiments without retraining the shared motion model.

Problem

• cross-embodiment
• video generation
• motion transfer
• embodiment adaptation
• scalability

Innovation

• cross-embodiment generation
• unpaired adaptation
• motion disentanglement
• branch-isolated attention
• embodied intelligence