no code implementations • 25 Apr 2024 • Junting Dong, Qi Fang, Zehuan Huang, Xudong Xu, Jingbo Wang, Sida Peng, Bo Dai
Previous works usually encode the human body and clothes as a holistic model and generate the whole model in a single-stage optimization, which makes them struggle for clothing editing and meanwhile lose fine-grained control over the whole generation process.
1 code implementation • 17 Oct 2023 • Xinhao Liu, Moonjun Gong, Qi Fang, Haoyu Xie, Yiming Li, Hang Zhao, Chen Feng
In this paper, we introduce a novel LiDAR perception task of Occupancy Completion and Forecasting (OCF) in the context of autonomous driving to unify these aspects into a cohesive framework.
1 code implementation • ICCV 2023 • Yiming Li, Qi Fang, Jiamu Bai, Siheng Chen, Felix Juefei-Xu, Chen Feng
This leads to our hypothesize-and-verify framework: perception results with and without collaboration from a random subset of teammates are compared until reaching a consensus.
1 code implementation • CVPR 2023 • Qi Fang, Kang Chen, Yinghui Fan, Qing Shuai, Jiefeng Li, Weidong Zhang
Despite various probabilistic methods for modeling the uncertainty and ambiguity in human mesh recovery, their overall precision is limited because existing formulations for joint rotations are either not constrained to SO(3) or difficult to learn for neural networks.
no code implementations • ICCV 2023 • Junting Dong, Qi Fang, Tianshuo Yang, Qing Shuai, Chengyu Qiao, Sida Peng
However, these methods usually rely on limited multi-view images typically collected in the studio or commercial high-quality 3D scans for training, which heavily prohibits their generalization capability for in-the-wild images.
no code implementations • SIGGRAPH 2022 • Zhize Zhou, Qing Shuai, Yize Wang, Qi Fang, Xiaopeng Ji, Fashuai Li, Hujun Bao, Xiaowei Zhou
The key challenge of this problem is to efficiently match 2D observations across multiple views.
Ranked #2 on 3D Multi-Person Pose Estimation on Shelf
1 code implementation • CVPR 2021 • Qi Fang, Qing Shuai, Junting Dong, Hujun Bao, Xiaowei Zhou
In this paper, we introduce the new task of reconstructing 3D human pose from a single image in which we can see the person and the person's image through a mirror.
1 code implementation • ECCV 2020 • Jianan Zhen, Qi Fang, Jiaming Sun, Wentao Liu, Wei Jiang, Hujun Bao, Xiaowei Zhou
Recovering multi-person 3D poses with absolute scales from a single RGB image is a challenging problem due to the inherent depth and scale ambiguity from a single view.
Ranked #11 on 3D Multi-Person Pose Estimation (absolute) on MuPoTS-3D