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Found 9 papers, 5 papers with code
Zero-shot Pose Transfer for Unrigged Stylized 3D Characters
We present a zero-shot approach that requires only the widely available deformed non-stylized avatars in training, and deforms stylized characters of significantly different shapes at inference.
ContactArt: Learning 3D Interaction Priors for Category-level Articulated Object and Hand Poses Estimation
We propose a new dataset and a novel approach to learning hand-object interaction priors for hand and articulated object pose estimation.
USEEK: Unsupervised SE(3)-Equivariant 3D Keypoints for Generalizable Manipulation
Can a robot manipulate intra-category unseen objects in arbitrary poses with the help of a mere demonstration of grasping pose on a single object instance?
Learning Continuous Grasping Function with a Dexterous Hand from Human Demonstrations
We will first convert the large-scale human-object interaction trajectories to robot demonstrations via motion retargeting, and then use these demonstrations to train CGF.
Learning Generalizable Dexterous Manipulation from Human Grasp Affordance
In this paper, we propose to learn dexterous manipulation using large-scale demonstrations with diverse 3D objects in a category, which are generated from a human grasp affordance model.
Multi-Person 3D Motion Prediction with Multi-Range Transformers
Thus, instead of predicting each human pose trajectory in isolation, we introduce a Multi-Range Transformers model which contains of a local-range encoder for individual motion and a global-range encoder for social interactions.
Hand-Object Contact Consistency Reasoning for Human Grasps Generation
Based on the hand-object contact consistency, we design novel objectives in training the human grasp generation model and also a new self-supervised task which allows the grasp generation network to be adjusted even during test time.
Synthesizing Long-Term 3D Human Motion and Interaction in 3D Scenes
Synthesizing 3D human motion plays an important role in many graphics applications as well as understanding human activity.
Neural Pose Transfer by Spatially Adaptive Instance Normalization
Pose transfer has been studied for decades, in which the pose of a source mesh is applied to a target mesh.
