Motion4Motion: Motion Transfer Across Subjects at Inference

2026-07-13

AI Summary

This work addresses the limitations of existing motion transfer methods, which rely on predefined human skeletal structures and annotated data, thereby struggling to generalize across species. To overcome these constraints, the authors propose Motion4Motion—a training-free, cross-species motion transfer framework that eschews explicit skeleton modeling in favor of optical flow representations derived directly from video. By aligning motion features across domains during inference, the method enables high-quality motion transfer between diverse subjects—including across species—without requiring retraining or fine-tuning. This approach significantly enhances generalization capability and practical flexibility, outperforming current baselines across a range of characters and unlocking novel applications in animation production and beyond.

Abstract

This work explores the motion transfer from one video to another, which is crucial in animation for diverse characters. Previously, video motion transfer has been largely explored between human and human-like characters, enabling a lot of applications in digital creation. However, these approaches encounter a main limitation. Specifically, related technical pipelines heavily rely on a predefined human skeleton structure and accordingly require skeleton-conditional model training. On the one hand, these methods are difficult to generalize to diverse characters, such as animals from different species, while preserving their unique motion styles. On the other hand, labeled data in diverse skeletons is limited, which additionally restricts the large-scale training for the task. In this paper, we jump out of the skeleton-based motion transfer framework and propose a training-free motion transfer framework, named Motion4Motion. Motion4Motionmodels the motion flow of the character in a video instead of skeletons, which makes motion transfer across species easier. Extensive experimental results and novel applications show our methods outperform baselines impressively. Project page is available at https://lhchen.top/Motion4Motion.

Problem

• motion transfer
• cross-species
• skeleton-free
• diverse characters
• video animation

Innovation

• motion transfer
• training-free
• motion flow
• cross-species animation
• skeleton-free