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Found 8 papers, 4 papers with code
Neural Implicit Representation for Building Digital Twins of Unknown Articulated Objects
We address the problem of building digital twins of unknown articulated objects from two RGBD scans of the object at different articulation states.
Towards Learning Geometric Eigen-Lengths Crucial for Fitting Tasks
In this work, we therefore for the first time formulate and propose a novel learning problem on this question and set up a benchmark suite including tasks, data, and evaluation metrics for studying the problem.
UniDexGrasp: Universal Robotic Dexterous Grasping via Learning Diverse Proposal Generation and Goal-Conditioned Policy
Trained on our synthesized large-scale dexterous grasp dataset, this model enables us to sample diverse and high-quality dexterous grasp poses for the object point cloud. For the second stage, we propose to replace the motion planning used in parallel gripper grasping with a goal-conditioned grasp policy, due to the complexity involved in dexterous grasping execution.
Tracking and Reconstructing Hand Object Interactions from Point Cloud Sequences in the Wild
We for the first time propose a point cloud based hand joint tracking network, HandTrackNet, to estimate the inter-frame hand joint motion.
Leveraging SE(3) Equivariance for Self-Supervised Category-Level Object Pose Estimation
Category-level object pose estimation aims to find 6D object poses of previously unseen object instances from known categories without access to object CAD models.
Leveraging SE(3) Equivariance for Self-supervised Category-Level Object Pose Estimation from Point Clouds
To reduce the huge amount of pose annotations needed for category-level learning, we propose for the first time a self-supervised learning framework to estimate category-level 6D object pose from single 3D point clouds.
CAPTRA: CAtegory-level Pose Tracking for Rigid and Articulated Objects from Point Clouds
For the first time, we propose a unified framework that can handle 9DoF pose tracking for novel rigid object instances as well as per-part pose tracking for articulated objects from known categories.
Unpaired Motion Style Transfer from Video to Animation
In this paper, we present a novel data-driven framework for motion style transfer, which learns from an unpaired collection of motions with style labels, and enables transferring motion styles not observed during training.
