no code implementations • 1 Apr 2024 • Yunze Liu, Changxi Chen, Chenjing Ding, Li Yi
Humanoid Reaction Synthesis is pivotal for creating highly interactive and empathetic robots that can seamlessly integrate into human environments, enhancing the way we live, work, and communicate.
no code implementations • 17 Jan 2024 • Yunze Liu, Changxi Chen, Zifan Wang, Li Yi
This paper introduces a novel approach named CrossVideo, which aims to enhance self-supervised cross-modal contrastive learning in the field of point cloud video understanding.
1 code implementation • 14 Dec 2023 • Yunze Liu, Changxi Chen, Li Yi
To support this task, we construct two datasets named HHI and CoChair and propose a unified method.
no code implementations • 12 Oct 2023 • Yuhao Dong, Zhuoyang Zhang, Yunze Liu, Li Yi
We integrate NSM4D with state-of-the-art 4D perception backbones, demonstrating significant improvements on various online perception benchmarks in indoor and outdoor settings.
no code implementations • ICCV 2023 • Yunze Liu, Junyu Chen, Zekai Zhang, Jingwei Huang, Li Yi
With such frames, we can factorize geometry and motion to facilitate a feature-space geometric reconstruction for more effective 4D learning.
no code implementations • CVPR 2023 • Zhuoyang Zhang, Yuhao Dong, Yunze Liu, Li Yi
Recent work on 4D point cloud sequences has attracted a lot of attention.
1 code implementation • 30 Jul 2022 • Hao Wen, Yunze Liu, Jingwei Huang, Bo Duan, Li Yi
This paper proposes a 4D backbone for long-term point cloud video understanding.
1 code implementation • CVPR 2022 • Yunze Liu, Yun Liu, Che Jiang, Kangbo Lyu, Weikang Wan, Hao Shen, Boqiang Liang, Zhoujie Fu, He Wang, Li Yi
We present HOI4D, a large-scale 4D egocentric dataset with rich annotations, to catalyze the research of category-level human-object interaction.
1 code implementation • ICCV 2021 • Yunze Liu, Qingnan Fan, Shanghang Zhang, Hao Dong, Thomas Funkhouser, Li Yi
Another approach is to concatenate all the modalities into a tuple and then contrast positive and negative tuple correspondences.
Ranked #70 on Semantic Segmentation on NYU Depth v2
no code implementations • 24 Dec 2020 • Yunze Liu, Li Yi, Shanghang Zhang, Qingnan Fan, Thomas Funkhouser, Hao Dong
Self-supervised representation learning is a critical problem in computer vision, as it provides a way to pretrain feature extractors on large unlabeled datasets that can be used as an initialization for more efficient and effective training on downstream tasks.