  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.
