2 citations · 11 across the 10 of their papers we have counts for
13 papers
Robust Camera-to-Mocap Calibration and Verification for Large-Scale Multi-Camera Data Capture
Tianyi Liu, Christopher Twigg, Patrick Grady +3
Optical motion capture (mocap) systems are widely used for ground-truth capture in AR/VR, SLAM and robotics datasets. These datasets require extrinsic calibration to align mocap co…
MHR: Momentum Human Rig
Aaron Ferguson, Ahmed A. A. Osman, Berta Bescos +44
We present MHR, a parametric human body model that combines the decoupled skeleton/shape paradigm of ATLAS with a flexible, modern rig and pose corrective system inspired by the Mo…
PressureVision++: Estimating Fingertip Pressure from Diverse RGB Images
Patrick Grady, Jeremy A. Collins, Chengcheng Tang +4
Touch plays a fundamental role in manipulation for humans; however, machine perception of contact and pressure typically requires invasive sensors. Recent research has shown that d…
MotionDeltaCNN: Sparse CNN Inference of Frame Differences in Moving Camera Videos
Mathias Parger, Chengcheng Tang, Thomas Neff +4
Convolutional neural network inference on video input is computationally expensive and requires high memory bandwidth. Recently, DeltaCNN managed to reduce the cost by only process…
Neural Correspondence Field for Object Pose Estimation
Lin Huang, Tomas Hodan, Lingni Ma +7
We propose a method for estimating the 6DoF pose of a rigid object with an available 3D model from a single RGB image. Unlike classical correspondence-based methods which predict 3…
Visual Pressure Estimation and Control for Soft Robotic Grippers
Patrick Grady, Jeremy A. Collins, Samarth Brahmbhatt +4
Soft robotic grippers facilitate contact-rich manipulation, including robust grasping of varied objects. Yet the beneficial compliance of a soft gripper also results in significant…
PressureVision: Estimating Hand Pressure from a Single RGB Image
Patrick Grady, Chengcheng Tang, Samarth Brahmbhatt +4
People often interact with their surroundings by applying pressure with their hands. While hand pressure can be measured by placing pressure sensors between the hand and the enviro…
DeltaCNN: End-to-End CNN Inference of Sparse Frame Differences in Videos
Mathias Parger, Chengcheng Tang, Christopher D. Twigg +3
Convolutional neural network inference on video data requires powerful hardware for real-time processing. Given the inherent coherence across consecutive frames, large parts of a v…
Identity-Disentangled Neural Deformation Model for Dynamic Meshes
Binbin Xu, Lingni Ma, Yuting Ye +3
Neural shape models can represent complex 3D shapes with a compact latent space. When applied to dynamically deforming shapes such as the human hands, however, they would need to p…
ContactOpt: Optimizing Contact to Improve Grasps
Patrick Grady, Chengcheng Tang, Christopher D. Twigg +3
Physical contact between hands and objects plays a critical role in human grasps. We show that optimizing the pose of a hand to achieve expected contact with an object can improve…
UNOC: Understanding Occlusion for Embodied Presence in Virtual Reality
Mathias Parger, Chengcheng Tang, Yuanlu Xu +5
Tracking body and hand motions in the 3D space is essential for social and self-presence in augmented and virtual environments. Unlike the popular 3D pose estimation setting, the p…
ContactPose: A Dataset of Grasps with Object Contact and Hand Pose
Samarth Brahmbhatt, Chengcheng Tang, Christopher D. Twigg +2
Grasping is natural for humans. However, it involves complex hand configurations and soft tissue deformation that can result in complicated regions of contact between the hand and…
Disentangling Pose from Appearance in Monochrome Hand Images
Yikang Li, Chris Twigg, Yuting Ye +2
Hand pose estimation from the monocular 2D image is challenging due to the variation in lighting, appearance, and background. While some success has been achieved using deep neural…