9 citations · 20 across the 4 of their papers we have counts for
7 papers · 1 filter
RePose: Learning Deep Kinematic Priors for Fast Human Pose Estimation
Hossam Isack, Christian Haene, Cem Keskin +4
We propose a novel efficient and lightweight model for human pose estimation from a single image. Our model is designed to achieve competitive results at a fraction of the number o…
Volumetric Capture of Humans with a Single RGBD Camera via Semi-Parametric Learning
Rohit Pandey, Anastasia Tkach, Shuoran Yang +9
Volumetric (4D) performance capture is fundamental for AR/VR content generation. Whereas previous work in 4D performance capture has shown impressive results in studio settings, th…
LookinGood: Enhancing Performance Capture with Real-time Neural Re-Rendering
Ricardo Martin-Brualla, Rohit Pandey, Shuoran Yang +14
Motivated by augmented and virtual reality applications such as telepresence, there has been a recent focus in real-time performance capture of humans under motion. However, given…
MIST: Multiple Instance Spatial Transformer Network
Baptiste Angles, Yuhe Jin, Simon Kornblith +2
We propose a deep network that can be trained to tackle image reconstruction and classification problems that involve detection of multiple object instances, without any supervisio…
StereoNet: Guided Hierarchical Refinement for Real-Time Edge-Aware Depth Prediction
Sameh Khamis, Sean Fanello, Christoph Rhemann +3
This paper presents StereoNet, the first end-to-end deep architecture for real-time stereo matching that runs at 60 fps on an NVidia Titan X, producing high-quality, edge-preserved…
ActiveStereoNet: End-to-End Self-Supervised Learning for Active Stereo Systems
Yinda Zhang, Sameh Khamis, Christoph Rhemann +7
In this paper we present ActiveStereoNet, the first deep learning solution for active stereo systems. Due to the lack of ground truth, our method is fully self-supervised, yet it p…