DMP: Directable Motion Retargeting through Motion Paraphrasing
Creating diverse and realistic human motions is a fundamental cornerstone of computer animation, with numerous applications in games, movies, and AR/VR. While motion capture is a valuable tool for capturing motions across varied body sizes, obtaining unique motion data for a variety of characters is often prohibitively expensive. Motion retargeting addresses this limitation by adapting existing motions to different character morphologies, however, existing approaches often involve trade-offs between motion realism, user control, and adaptability to artistic needs. In this work, we propose Directable Motion Paraphrasing (DMP), a novel motion retargeting framework based on the concept of motion paraphrasing, analogous to text paraphrasing, where the core semantics of a motion are preserved while allowing expressive, user-directed variations. Our framework constructs a large-scale motion paraphrasing dataset, which captures the diversity of human motion across different body shapes, and trains a diffusion-based generative model that learns both invariances and variations in motion. To enable user control during inference, we introduce a flexible mechanism for specifying spatio-temporal constraints, such as joint positions, rotations, and object interactions, which can be incorporated into the generative process through masked inpainting and loss guidance. We demonstrate the effectiveness of our framework through various examples, showing its ability to produce realistic, diverse, and controllable retargeted motions that meet the artistic demands of animation pipelines. Extensive experiments demonstrate the system’s flexibility, motion plausibility, and directability, highlighting its potential as a tool for intuitive and high-quality motion retargeting.