1 code implementation • 3 Dec 2023 • Zengyi Qin, Wenliang Zhao, Xumin Yu, Xin Sun
The voice styles are not directly copied from and constrained by the style of the reference speaker.
1 code implementation • ICCV 2023 • Ziyi Wang, Xumin Yu, Yongming Rao, Jie zhou, Jiwen Lu
In this paper, we propose a novel 3D-to-2D generative pre-training method that is adaptable to any point cloud model.
Ranked #6 on 3D Part Segmentation on ShapeNet-Part
1 code implementation • 11 Jan 2023 • Xumin Yu, Yongming Rao, Ziyi Wang, Jiwen Lu, Jie zhou
In this paper, we present a new method that reformulates point cloud completion as a set-to-set translation problem and design a new model, called PoinTr, which adopts a Transformer encoder-decoder architecture for point cloud completion.
Ranked #2 on Point Cloud Completion on ShapeNet
1 code implementation • 4 Aug 2022 • Ziyi Wang, Xumin Yu, Yongming Rao, Jie zhou, Jiwen Lu
Nowadays, pre-training big models on large-scale datasets has become a crucial topic in deep learning.
Ranked #18 on 3D Point Cloud Classification on ScanObjectNN (using extra training data)
1 code implementation • 17 Jul 2022 • Yansong Tang, Xingyu Liu, Xumin Yu, Danyang Zhang, Jiwen Lu, Jie zhou
Different from the conventional adversarial learning-based approaches for UDA, we utilize a self-supervision scheme to reduce the domain shift between two skeleton-based action datasets.
1 code implementation • CVPR 2022 • Ziyi Wang, Yongming Rao, Xumin Yu, Jie zhou, Jiwen Lu
Conventional point cloud semantic segmentation methods usually employ an encoder-decoder architecture, where mid-level features are locally aggregated to extract geometric information.
1 code implementation • CVPR 2022 • Jinglin Xu, Yongming Rao, Xumin Yu, Guangyi Chen, Jie zhou, Jiwen Lu
Most existing action quality assessment methods rely on the deep features of an entire video to predict the score, which is less reliable due to the non-transparent inference process and poor interpretability.
2 code implementations • 22 Dec 2021 • Liang Pan, Tong Wu, Zhongang Cai, Ziwei Liu, Xumin Yu, Yongming Rao, Jiwen Lu, Jie zhou, Mingye Xu, Xiaoyuan Luo, Kexue Fu, Peng Gao, Manning Wang, Yali Wang, Yu Qiao, Junsheng Zhou, Xin Wen, Peng Xiang, Yu-Shen Liu, Zhizhong Han, Yuanjie Yan, Junyi An, Lifa Zhu, Changwei Lin, Dongrui Liu, Xin Li, Francisco Gómez-Fernández, Qinlong Wang, Yang Yang
Based on the MVP dataset, this paper reports methods and results in the Multi-View Partial Point Cloud Challenge 2021 on Completion and Registration.
2 code implementations • CVPR 2022 • Xumin Yu, Lulu Tang, Yongming Rao, Tiejun Huang, Jie zhou, Jiwen Lu
Inspired by BERT, we devise a Masked Point Modeling (MPM) task to pre-train point cloud Transformers.
Ranked #13 on Few-Shot 3D Point Cloud Classification on ModelNet40 5-way (10-shot) (using extra training data)
3D Point Cloud Linear Classification Few-Shot 3D Point Cloud Classification +2
1 code implementation • ICCV 2021 • Xumin Yu, Yongming Rao, Ziyi Wang, Zuyan Liu, Jiwen Lu, Jie zhou
In this paper, we present a new method that reformulates point cloud completion as a set-to-set translation problem and design a new model, called PoinTr that adopts a transformer encoder-decoder architecture for point cloud completion.
Ranked #1 on Point Cloud Completion on ShapeNet (Chamfer Distance L2 metric)
1 code implementation • ICCV 2021 • Xumin Yu, Yongming Rao, Wenliang Zhao, Jiwen Lu, Jie zhou
Assessing action quality is challenging due to the subtle differences between videos and large variations in scores.
Ranked #2 on Action Quality Assessment on MTL-AQA