Search Results for author: Christopher D. Twigg

Found 8 papers, 3 papers with code

PressureVision++: Estimating Fingertip Pressure from Diverse RGB Images

no code implementations5 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.

Mixed Reality

MotionDeltaCNN: Sparse CNN Inference of Frame Differences in Moving Camera Videos with Spherical Buffers and Padded Convolutions

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.

MotionDeltaCNN: Sparse CNN Inference of Frame Differences in Moving Camera Videos

no code implementations18 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.

PressureVision: Estimating Hand Pressure from a Single RGB Image

1 code implementation19 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.

Contact mechanics

DeltaCNN: End-to-End CNN Inference of Sparse Frame Differences in Videos

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.

Identity-Disentangled Neural Deformation Model for Dynamic Meshes

no code implementations30 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.

Disentanglement

ContactOpt: Optimizing Contact to Improve Grasps

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.

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