1 code implementation • ICCV 2023 • Shubhra Aich, Jesus Ruiz-Santaquiteria, Zhenyu Lu, Prachi Garg, K J Joseph, Alvaro Fernandez Garcia, Vineeth N Balasubramanian, Kenrick Kin, Chengde Wan, Necati Cihan Camgoz, Shugao Ma, Fernando de la Torre
Our sampling scheme outperforms SOTA methods significantly on two 3D skeleton gesture datasets, the publicly available SHREC 2017, and EgoGesture3D -- which we extract from a publicly available RGBD dataset.
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 • ECCV 2020 • Chengde Wan, Thomas Probst, Luc van Gool, Angela Yao
In the first stage, the network estimates a dense correspondence field for every pixel on the depth map or image grid to the mesh grid.
no code implementations • CVPR 2019 • Chengde Wan, Thomas Probst, Luc Van Gool, Angela Yao
We present a self-supervision method for 3D hand pose estimation from depth maps.
1 code implementation • CVPR 2018 • Chengde Wan, Thomas Probst, Luc van Gool, Angela Yao
Specifically, we decompose the pose parameters into a set of per-pixel estimations, i. e., 2D heat maps, 3D heat maps and unit 3D directional vector fields.
Ranked #4 on Hand Pose Estimation on MSRA Hands
no code implementations • CVPR 2017 • Chengde Wan, Thomas Probst, Luc van Gool, Angela Yao
Regressing the hand pose can then be done by learning a discriminator to estimate the posterior of the latent pose given some depth maps.
no code implementations • CVPR 2017 • Zhiwu Huang, Chengde Wan, Thomas Probst, Luc van Gool
In recent years, skeleton-based action recognition has become a popular 3D classification problem.
no code implementations • 10 Apr 2016 • Chengde Wan, Angela Yao, Luc van Gool
We present a hierarchical regression framework for estimating hand joint positions from single depth images based on local surface normals.
no code implementations • 17 Nov 2015 • Jinghua Wang, Abrar Abdul Nabi, Gang Wang, Chengde Wan, Tian-Tsong Ng
Given attributes as representations, we propose to learn a ranking SPN (sum product networks) to rank pairs of fashion images.