no code implementations • 5 Jan 2023 • Patrick Grady, Jeremy A. Collins, Chengcheng Tang, Christopher D. Twigg, Kunal Aneja, James Hays, Charles C. Kemp
We present a novel approach that enables diverse data to be captured with only an RGB camera and a cooperative participant.
no code implementations • ICCV 2023 • Mathias Parger, Chengcheng Tang, Thomas Neff, Christopher D. Twigg, Cem Keskin, Robert Wang, Markus Steinberger
Moving cameras add new challenges in how to fuse newly unveiled image regions with already processed regions efficiently to minimize the update rate - without increasing memory overhead and without knowing the camera extrinsics of future frames.
no code implementations • 18 Oct 2022 • Mathias Parger, Chengcheng Tang, Thomas Neff, Christopher D. Twigg, Cem Keskin, Robert Wang, Markus Steinberger
Moving cameras add new challenges in how to fuse newly unveiled image regions with already processed regions efficiently to minimize the update rate - without increasing memory overhead and without knowing the camera extrinsics of future frames.
1 code implementation • 19 Mar 2022 • Patrick Grady, Chengcheng Tang, Samarth Brahmbhatt, Christopher D. Twigg, Chengde Wan, James Hays, Charles C. Kemp
We also show that the output of our model depends on the appearance of the hand and cast shadows near contact regions.
no code implementations • CVPR 2022 • Mathias Parger, Chengcheng Tang, Christopher D. Twigg, Cem Keskin, Robert Wang, Markus Steinberger
With DeltaCNN, we present a sparse convolutional neural network framework that enables sparse frame-by-frame updates to accelerate video inference in practice.
no code implementations • 30 Sep 2021 • Binbin Xu, Lingni Ma, Yuting Ye, Tanner Schmidt, Christopher D. Twigg, Steven Lovegrove
When applied to dynamically deforming shapes such as the human hands, however, they would need to preserve temporal coherence of the deformation as well as the intrinsic identity of the subject.
1 code implementation • CVPR 2021 • Patrick Grady, Chengcheng Tang, Christopher D. Twigg, Minh Vo, Samarth Brahmbhatt, Charles C. Kemp
Given a hand mesh and an object mesh, a deep model trained on ground truth contact data infers desirable contact across the surfaces of the meshes.
2 code implementations • ECCV 2020 • Samarth Brahmbhatt, Chengcheng Tang, Christopher D. Twigg, Charles C. Kemp, James Hays
We introduce ContactPose, the first dataset of hand-object contact paired with hand pose, object pose, and RGB-D images.
Ranked #1 on Grasp Contact Prediction on ContactPose