no code implementations • 27 Nov 2023 • Jane Wu, Diego Thomas, Ronald Fedkiw
We present a novel deep learning-based approach to the 3D reconstruction of clothed humans using weak supervision via 2D normal maps.
no code implementations • 8 Aug 2023 • João Paulo Lima, Diego Thomas, Hideaki Uchiyama, Veronica Teichrieb
In this context, we investigate two approaches for automatically labeling target data: pseudo-labeling using a supervised detector and automatic labeling using an untrained detector (that can be applied out of the box without any training).
no code implementations • 7 Jul 2021 • Akihiko Sayo, Diego Thomas, Hiroshi Kawasaki, Yuta Nakashima, Katsushi Ikeuchi
We propose a new 2D pose refinement network that learns to predict the human bias in the estimated 2D pose.
Ranked #67 on 3D Human Pose Estimation on Human3.6M
1 code implementation • CVPR 2020 • Hayato Onizuka, Zehra Hayirci, Diego Thomas, Akihiro Sugimoto, Hideaki Uchiyama, Rin-ichiro Taniguchi
In this paper, we propose the tetrahedral outer shell volumetric truncated signed distance function (TetraTSDF) model for the human body, and its corresponding part connection network (PCN) for 3D human body shape regression.
1 code implementation • 22 Apr 2020 • Diego Thomas
We propose a method to build in real-time animated 3D head models using a consumer-grade RGB-D camera.
no code implementations • CVPR 2016 • Diego Thomas, Rin-ichiro Taniguchi
Our framework is the first one to provide simultaneously comprehensive facial motion tracking and a detailed 3D model of the user's head.