Shape-Conditioned Human Motion Diffusion Model with Mesh Representation
| dc.contributor.author | Xue, Kebing | en_US |
| dc.contributor.author | Seo, Hyewon | en_US |
| dc.contributor.author | Bobenrieth, Cédric | en_US |
| dc.contributor.author | Luo, Guoliang | en_US |
| dc.contributor.editor | Bousseau, Adrien | en_US |
| dc.contributor.editor | Day, Angela | en_US |
| dc.date.accessioned | 2025-05-09T09:15:15Z | |
| dc.date.available | 2025-05-09T09:15:15Z | |
| dc.date.issued | 2025 | |
| dc.description.abstract | Human motion generation is a key task in computer graphics. While various conditioning signals such as text, action class, or audio have been used to harness the generation process, most existing methods neglect the case where a specific body is desired to perform the motion. Additionally, they rely on skeleton-based pose representations, necessitating additional steps to produce renderable meshes of the intended body shape. Given that human motion involves a complex interplay of bones, joints, and muscles, focusing solely on the skeleton during generation neglects the rich information carried by muscles and soft tissues, as well as their influence on movement, ultimately limiting the variability and precision of the generated motions. In this paper, we introduce Shape-conditioned Motion Diffusion model (SMD), which enables the generation of human motion directly in the form of a mesh sequence, conditioned on both a text prompt and a body mesh. To fully exploit the mesh representation while minimizing resource costs, we employ spectral representation using the graph Laplacian to encode body meshes into the learning process. Unlike retargeting methods, our model does not require source motion data and generates a variety of desired semantic motions that is inherently tailored to the given identity shape. Extensive experimental evaluations show that the SMD model not only maintains the body shape consistently with the conditioning input across motion frames but also achieves competitive performance in text-to-motion and action-to-motion tasks compared to state-of-the-art methods. | en_US |
| dc.description.number | 2 | |
| dc.description.sectionheaders | Bringing Motion to Life: Motion Reconstruction and Control | |
| dc.description.seriesinformation | Computer Graphics Forum | |
| dc.description.volume | 44 | |
| dc.identifier.doi | 10.1111/cgf.70065 | |
| dc.identifier.issn | 1467-8659 | |
| dc.identifier.pages | 11 pages | |
| dc.identifier.uri | https://doi.org/10.1111/cgf.70065 | |
| dc.identifier.uri | https://diglib.eg.org/handle/10.1111/cgf70065 | |
| dc.publisher | The Eurographics Association and John Wiley & Sons Ltd. | en_US |
| dc.subject | CCS Concepts: Computing methodologies → Modeling and simulation; Artificial intelligence | |
| dc.subject | Computing methodologies → Modeling and simulation | |
| dc.subject | Artificial intelligence | |
| dc.title | Shape-Conditioned Human Motion Diffusion Model with Mesh Representation | en_US |