no code implementations • 28 Mar 2024 • Yujin Chen, Yinyu Nie, Benjamin Ummenhofer, Reiner Birkl, Michael Paulitsch, Matthias Müller, Matthias Nießner
In Mesh2NeRF, we propose an analytic solution to directly obtain ground-truth radiance fields from 3D meshes, characterizing the density field with an occupancy function featuring a defined surface thickness, and determining view-dependent color through a reflection function considering both the mesh and environment lighting.
no code implementations • ICCV 2023 • Zhisheng Huang, Yujin Chen, Di Kang, Jinlu Zhang, Zhigang Tu
We propose PHRIT, a novel approach for parametric hand mesh modeling with an implicit template that combines the advantages of both parametric meshes and implicit representations.
no code implementations • 7 Feb 2023 • Junwen Huang, Alexey Artemov, Yujin Chen, Shuaifeng Zhi, Kai Xu, Matthias Nießner
In this work, we explore a central 3D scene modeling task, namely, semantic scene reconstruction without using any 3D annotations.
1 code implementation • CVPR 2022 • Jinlu Zhang, Zhigang Tu, Jianyu Yang, Yujin Chen, Junsong Yuan
Recent transformer-based solutions have been introduced to estimate 3D human pose from 2D keypoint sequence by considering body joints among all frames globally to learn spatio-temporal correlation.
Ranked #6 on Monocular 3D Human Pose Estimation on Human3.6M
no code implementations • 8 Feb 2022 • Zhigang Tu, Jiaxu Zhang, Hongyan Li, Yujin Chen, Junsong Yuan
In recent years, graph convolutional networks (GCNs) play an increasingly critical role in skeleton-based human action recognition.
no code implementations • 24 Jan 2022 • Zhigang Tu, Zhisheng Huang, Yujin Chen, Di Kang, Linchao Bao, Bisheng Yang, Junsong Yuan
We present a method for reconstructing accurate and consistent 3D hands from a monocular video.
no code implementations • 6 Dec 2021 • Yujin Chen, Matthias Nießner, Angela Dai
We present a new approach to instill 4D dynamic object priors into learned 3D representations by unsupervised pre-training.
Ranked #21 on 3D Instance Segmentation on ScanNet(v2)
1 code implementation • CVPR 2021 • Yujin Chen, Zhigang Tu, Di Kang, Linchao Bao, Ying Zhang, Xuefei Zhe, Ruizhi Chen, Junsong Yuan
For the first time, we demonstrate the feasibility of training an accurate 3D hand reconstruction network without relying on manual annotations.
no code implementations • ICCV 2021 • Ping Chen, Yujin Chen, Dong Yang, Fangyin Wu, Qin Li, Qingpei Xia, Yong Tan
Reconstructing a high-precision and high-fidelity 3D human hand from a color image plays a central role in replicating a realistic virtual hand in human-computer interaction and virtual reality applications.
no code implementations • 28 Jun 2020 • Yujin Chen, Zhigang Tu, Di Kang, Ruizhi Chen, Linchao Bao, Zhengyou Zhang, Junsong Yuan
In this work, we propose to consider hand and object jointly in feature space and explore the reciprocity of the two branches.
no code implementations • ICCV 2019 • Yujin Chen, Zhigang Tu, Liuhao Ge, Dejun Zhang, Ruizhi Chen, Junsong Yuan
Since the HFE and the HFD can be trained without 3D hand pose annotation, the proposed method is able to make the best of unannotated data during the training phase.
1 code implementation • journal 2018 • Yujin Chen, Ruizhi Chen, Mengyun Liu, Aoran Xiao, Dewen Wu and Shuheng Zhao
The first one is CNN-based image retrieval phase, CNN features extracted by pre-trained deep convolutional neural networks (DCNNs) from images are utilized to compare the similarity, and the output of this part are the matched images of the target image.