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Found 8 papers, 3 papers with code
3D Whole-body Grasp Synthesis with Directional Controllability
We tackle these with a novel method called CWGrasp.
WANDR: Intention-guided Human Motion Generation
To address this, we introduce WANDR, a data-driven model that takes an avatar's initial pose and a goal's 3D position and generates natural human motions that place the end effector (wrist) on the goal location.
GRIP: Generating Interaction Poses Using Spatial Cues and Latent Consistency
In contrast, we introduce GRIP, a learning-based method that takes, as input, the 3D motion of the body and the object, and synthesizes realistic motion for both hands before, during, and after object interaction.
3D Human Pose Estimation via Intuitive Physics
Inspired by biomechanics, we infer the pressure heatmap on the body, the Center of Pressure (CoP) from the heatmap, and the SMPL body's Center of Mass (CoM).
Ranked #3 on
3D Human Pose Estimation
on RICH
ARCTIC: A Dataset for Dexterous Bimanual Hand-Object Manipulation
In part this is because there exist no datasets with ground-truth 3D annotations for the study of physically consistent and synchronised motion of hands and articulated objects.
GOAL: Generating 4D Whole-Body Motion for Hand-Object Grasping
This is challenging, as it requires the avatar to walk towards the object with foot-ground contact, orient the head towards it, reach out, and grasp it with a realistic hand pose and hand-object contact.
Human Leg Motion Tracking by Fusing IMUs and RGB Camera Data Using Extended Kalman Filter
Human motion capture is frequently used to study rehabilitation and clinical problems, as well as to provide realistic animation for the entertainment industry.
GRAB: A Dataset of Whole-Body Human Grasping of Objects
Training computers to understand, model, and synthesize human grasping requires a rich dataset containing complex 3D object shapes, detailed contact information, hand pose and shape, and the 3D body motion over time.
