The ability to synthesize long-term human motion sequences in real-world scenes can facilitate numerous applications.
no code implementations • • 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.
Reducing inconsistencies in the behavior of different versions of an AI system can be as important in practice as reducing its overall error.
We propose a new loss function, called motion loss, for the problem of monocular 3D Human pose estimation from 2D pose.
Ranked #13 on 3D Human Pose Estimation on Human3.6M
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
Dynamics of human body skeletons convey significant information for human action recognition.
This work addresses unconstrained fashion landmark detection, where clothing bounding boxes are not provided in both training and test.
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.