no code implementations • 8 Apr 2024 • Heyuan Li, Ce Chen, Tianhao Shi, Yuda Qiu, Sizhe An, GuanYing Chen, Xiaoguang Han
We further introduce a view-image consistency loss for the discriminator to emphasize the correspondence of the camera parameters and the images.
1 code implementation • CVPR 2023 • Shuhong Chen, Kevin Zhang, Yichun Shi, Heng Wang, Yiheng Zhu, Guoxian Song, Sizhe An, Janus Kristjansson, Xiao Yang, Matthias Zwicker
We propose PAniC-3D, a system to reconstruct stylized 3D character heads directly from illustrated (p)ortraits of (ani)me (c)haracters.
1 code implementation • 23 Mar 2023 • Sizhe An, Hongyi Xu, Yichun Shi, Guoxian Song, Umit Ogras, Linjie Luo
We propose PanoHead, the first 3D-aware generative model that enables high-quality view-consistent image synthesis of full heads in $360^\circ$ with diverse appearance and detailed geometry using only in-the-wild unstructured images for training.
1 code implementation • CVPR 2023 • Sizhe An, Hongyi Xu, Yichun Shi, Guoxian Song, Umit Y. Ogras, Linjie Luo
We propose PanoHead, the first 3D-aware generative model that enables high-quality view-consistent image synthesis of full heads in 360deg with diverse appearance and detailed geometry using only in-the-wild unstructured images for training.
no code implementations • 15 Oct 2022 • Sizhe An, Yin Li, Umit Ogras
To bridge the gap, we present mRI, a multi-modal 3D human pose estimation dataset with mmWave, RGB-D, and Inertial Sensors.
no code implementations • 29 Apr 2022 • Sizhe An, Umit Y. Ogras
Millimeter-Wave (mmWave) radar can enable high-resolution human pose estimation with low cost and computational requirements.
1 code implementation • 23 Feb 2021 • Sizhe An, Yigit Tuncel, Toygun Basaklar, Gokul Krishna Krishnakumar, Ganapati Bhat, Umit Ogras
Movement disorders, such as Parkinson's disease, affect more than 10 million people worldwide.
no code implementations • 5 Dec 2020 • Sizhe An, Ganapati Bhat, Suat Gumussoy, Umit Ogras
The typical approach is training a HAR classifier offline with known users and then using the same classifier for new users.