no code implementations • 11 Dec 2023 • Xiaogang Peng, Yiming Xie, Zizhao Wu, Varun Jampani, Deqing Sun, Huaizu Jiang
We also develop an affordance prediction diffusion model (APDM) to predict the contacting area between the human and object during the interactions driven by the textual prompt.
1 code implementation • 23 Jun 2023 • Xiaogang Peng, Xiao Zhou, Yikai Luo, Hao Wen, Yu Ding, Zizhao Wu
We believe that the proposed MI-Motion benchmark dataset and baseline will facilitate future research in this area, ultimately leading to better understanding and modeling of multi-person interactions.
1 code implementation • 30 May 2023 • Xiaogang Peng, Hao Wen, Yikai Luo, Xiao Zhou, Keyang Yu, Ping Yang, Zizhao Wu
To overcome this, we propose HyperVD, a novel framework that learns snippet embeddings in hyperbolic space to improve model discrimination.
2 code implementations • CVPR 2023 • Xiaogang Peng, Siyuan Mao, Zizhao Wu
Specifically, we construct a Temporal Body Partition Module that transforms all the pose sequences into a Multi-Person Body-Part sequence to retain spatial and temporal information based on body semantics.
no code implementations • 22 Nov 2022 • Honggu Zhou, Xiaogang Peng, Jiawei Mao, Zizhao Wu, Ming Zeng
To solve it, we proposed PointCMC, a novel cross-modal method to model multi-scale correspondences across modalities for self-supervised point cloud representation learning.
no code implementations • 19 Aug 2022 • Xiaogang Peng, Yaodi Shen, Haoran Wang, Binling Nie, Yigang Wang, Zizhao Wu
Most prior methods only involve learning local pose dynamics for individual motion (without global body trajectory) and also struggle to capture complex interaction dependencies for social interactions.
no code implementations • 21 Mar 2022 • Cheng Zhang, Jian Shi, Xuan Deng, Zizhao Wu
In computer-aided design (CAD) community, the point cloud data is pervasively applied in reverse engineering, where the point cloud analysis plays an important role.
no code implementations • CVPR 2022 • Zhang Cheng, Haocheng Wan, Xinyi Shen, Zizhao Wu
Extensive experiments demonstrate that our network achieves comparable accuracy on general point cloud learning tasks with 9. 2x speed-up than previous point Transformers.
Ranked #1 on Semantic Segmentation on ShapeNet
2 code implementations • 13 Aug 2021 • Cheng Zhang, Haocheng Wan, Xinyi Shen, Zizhao Wu
The recently developed pure Transformer architectures have attained promising accuracy on point cloud learning benchmarks compared to convolutional neural networks.
Ranked #18 on 3D Part Segmentation on ShapeNet-Part