GraspDiff: Grasping Generation for Hand-Object Interaction With Multimodal Guided Diffusion.

Grasping generation holds significant importance in both robotics and AI-generated content. While pure network paradigms based on VAEs or GANs ensure diversity in outcomes, they often fall short of achieving plausibility. Additionally, although those two-step paradigms that first predict contact and then optimize distance yield plausible results, they are always known to be time-consuming.

Skeleton Extraction for Articulated Objects With the Spherical Unwrapping Profiles.

Embedding unified skeletons into unregistered scans is fundamental to finding correspondences, depicting motions, and capturing underlying structures among the articulated objects in the same category. Some existing approaches rely on laborious registration to adapt a predefined LBS model to each input, while others require the input to be set to a canonical pose, e.g.