24 code implementations • 23 Jan 2018 • Sijie Yan, Yuanjun Xiong, Dahua Lin
Dynamics of human body skeletons convey significant information for human action recognition.
4 code implementations • 10 Aug 2016 • Ziwei Liu, Sijie Yan, Ping Luo, Xiaogang Wang, Xiaoou Tang
Fashion landmark is also compared to clothing bounding boxes and human joints in two applications, fashion attribute prediction and clothes retrieval, showing that fashion landmark is a more discriminative representation to understand fashion images.
2 code implementations • 7 Aug 2017 • Sijie Yan, Ziwei Liu, Ping Luo, Shi Qiu, Xiaogang Wang, Xiaoou Tang
This work addresses unconstrained fashion landmark detection, where clothing bounding boxes are not provided in both training and test.
1 code implementation • ECCV 2020 • Jingbo Wang, Sijie Yan, Yuanjun Xiong, Dahua Lin
We propose a new loss function, called motion loss, for the problem of monocular 3D Human pose estimation from 2D pose.
Ranked #19 on 3D Human Pose Estimation on Human3.6M
no code implementations • ICCV 2019 2019 • Sijie Yan, Zhizhong Li, Yuanjun Xiong, Huahan Yan
It captures the temporal structure at multiple scales through the GP prior and the temporal convolutions; and establishes the spatial connection between the latent vectors and the skeleton graphs via a novel graph refining scheme.
Ranked #2 on Human action generation on NTU RGB+D
no code implementations • CVPR 2021 • Sijie Yan, Yuanjun Xiong, Kaustav Kundu, Shuo Yang, Siqi Deng, Meng Wang, Wei Xia, Stefano Soatto
Reducing inconsistencies in the behavior of different versions of an AI system can be as important in practice as reducing its overall error.
no code implementations • CVPR 2021 • Jingbo Wang, Sijie Yan, Bo Dai, Dahua Lin
We revisit human motion synthesis, a task useful in various real world applications, in this paper.
no code implementations • ICCV 2021 • Yujun Cai, Yiwei Wang, Yiheng Zhu, Tat-Jen Cham, Jianfei Cai, Junsong Yuan, Jun Liu, Chuanxia Zheng, Sijie Yan, Henghui Ding, Xiaohui Shen, Ding Liu, Nadia Magnenat Thalmann
Notably, by considering this problem as a conditional generation process, we estimate a parametric distribution of the missing regions based on the input conditions, from which to sample and synthesize the full motion series.
no code implementations • CVPR 2022 • Jingbo Wang, Yu Rong, Jingyuan Liu, Sijie Yan, Dahua Lin, Bo Dai
The ability to synthesize long-term human motion sequences in real-world scenes can facilitate numerous applications.