Shape Conditioned Human Motion Generation with Diffusion Model
This work addresses the need for more realistic and precise human motion generation in computer graphics and vision, though it is incremental as it builds on existing diffusion models with a novel conditioning approach.
The paper tackles the problem of generating human motion sequences directly in mesh format, conditioned on a target mesh, to overcome limitations of skeleton-based methods; it proposes a Shape-conditioned Motion Diffusion model (SMD) that achieves competitive performance in text-to-motion and action-to-motion tasks compared to state-of-the-art methods.
Human motion synthesis is an important task in computer graphics and computer vision. While focusing on various conditioning signals such as text, action class, or audio to guide the generation process, most existing methods utilize skeleton-based pose representation, requiring additional skinning to produce renderable meshes. Given that human motion is a complex interplay of bones, joints, and muscles, considering solely the skeleton for generation may neglect their inherent interdependency, which can limit the variability and precision of the generated results. To address this issue, we propose a Shape-conditioned Motion Diffusion model (SMD), which enables the generation of motion sequences directly in mesh format, conditioned on a specified target mesh. In SMD, the input meshes are transformed into spectral coefficients using graph Laplacian, to efficiently represent meshes. Subsequently, we propose a Spectral-Temporal Autoencoder (STAE) to leverage cross-temporal dependencies within the spectral domain. Extensive experimental evaluations show that SMD not only produces vivid and realistic motions but also achieves competitive performance in text-to-motion and action-to-motion tasks when compared to state-of-the-art methods.