2 citations · 3 across the 6 of their papers we have counts for
6 papers
Pb4U-GNet: Resolution-Adaptive Garment Simulation via Propagation-before-Update Graph Network
Aoran Liu, Kun Hu, Clinton Ansun Mo +3
Garment simulation is fundamental to various applications in computer vision and graphics, from virtual try-on to digital human modelling. However, conventional physics-based metho…
PUMPS: Skeleton-Agnostic Point-based Universal Motion Pre-Training for Synthesis in Human Motion Tasks
Clinton Ansun Mo, Kun Hu, Chengjiang Long +3
Motion skeletons drive 3D character animation by transforming bone hierarchies, but differences in proportions or structure make motion data hard to transfer across skeletons, posi…
Extended Short- and Long-Range Mesh Learning for Fast and Generalized Garment Simulation
Aoran Liu, Kun Hu, Clinton Mo +2
3D garment simulation is a critical component for producing cloth-based graphics. Recent advancements in graph neural networks (GNNs) offer a promising approach for efficient garme…
Motion Keyframe Interpolation for Any Human Skeleton via Temporally Consistent Point Cloud Sampling and Reconstruction
Clinton Mo, Kun Hu, Chengjiang Long +2
In the character animation field, modern supervised keyframe interpolation models have demonstrated exceptional performance in constructing natural human motions from sparse pose d…
Terrain Diffusion Network: Climatic-Aware Terrain Generation with Geological Sketch Guidance
Zexin Hu, Kun Hu, Clinton Mo +2
Sketch-based terrain generation seeks to create realistic landscapes for virtual environments in various applications such as computer games, animation and virtual reality. Recentl…
Continuous Intermediate Token Learning with Implicit Motion Manifold for Keyframe Based Motion Interpolation
Clinton Ansun Mo, Kun Hu, Chengjiang Long +1
Deriving sophisticated 3D motions from sparse keyframes is a particularly challenging problem, due to continuity and exceptionally skeletal precision. The action features are often…