no code implementations • 5 Sep 2021 • Kolby Nottingham, Litian Liang, Daeyun Shin, Charless C. Fowlkes, Roy Fox, Sameer Singh
Natural language instruction following tasks serve as a valuable test-bed for grounded language and robotics research.
no code implementations • 7 Apr 2020 • Zhe Wang, Daeyun Shin, Charless C. Fowlkes
Monocular estimation of 3d human pose has attracted increased attention with the availability of large ground-truth motion capture datasets.
Ranked #1 on
3D Human Pose Estimation
on Geometric Pose Affordance
(MPJPE metric)
no code implementations • CVPR 2020 • Yunhan Zhao, Shu Kong, Daeyun Shin, Charless Fowlkes
In this setting, we find that existing domain translation approaches are difficult to train and offer little advantage over simple baselines that use a mix of real and synthetic data.
no code implementations • 19 May 2019 • Zhe Wang, Liyan Chen, Shaurya Rathore, Daeyun Shin, Charless Fowlkes
Full 3D estimation of human pose from a single image remains a challenging task despite many recent advances.
no code implementations • ICCV 2019 • Daeyun Shin, Zhile Ren, Erik B. Sudderth, Charless C. Fowlkes
We tackle the problem of automatically reconstructing a complete 3D model of a scene from a single RGB image.
no code implementations • CVPR 2018 • Daeyun Shin, Charless C. Fowlkes, Derek Hoiem
The goal of this paper is to compare surface-based and volumetric 3D object shape representations, as well as viewer-centered and object-centered reference frames for single-view 3D shape prediction.
no code implementations • 15 Jun 2016 • Tanmay Gupta, Daeyun Shin, Naren Sivagnanadasan, Derek Hoiem
The resulting depth maps are then fused using a proposed implicit surface function that is robust to estimation error, producing a smooth surface reconstruction of the entire scene.
no code implementations • CVPR 2015 • Jason Rock, Tanmay Gupta, Justin Thorsen, JunYoung Gwak, Daeyun Shin, Derek Hoiem
Our goal is to recover a complete 3D model from a depth image of an object.