Kaichun Mo 莫凯淳

kaichun@cs.stanford.edu
personal page

Research interests

My research interests include Computer Vision and Graphics, Robotics, Machine Learning and Artificial Intelligence. In particular, I am interested in 3D vision and graphics, deep learning, geometry processing, reinforcement learning and robotics.

Recent publications

Yining Hong, Kaichun Mo, Li Yi, Leonidas J. Guibas, Antonio Torralba, Joshua Tenenbaum and Chuang Gan, Fixing Malfunctional Objects With Learned Physical Simulation and Functional Prediction, The Conference on Computer Vision and Pattern Recognition (CVPR) 2022   
Qi Li*, Kaichun Mo*, Yanchao Yang, Hang Zhao and Leonidas J. Guibas, IFR-Explore: Learning Inter-object Functional Relationships in 3D Indoor Scenes, International Conference on Learning Representations (ICLR) 2022   
Ruihai Wu*, Yan Zhao*, Kaichun Mo*, Zizheng Guo, Yian Wang, Tianhao Wu, Qingnan Fan, Xuelin Chen, Leonidas J. Guibas and Hao Dong, VAT-Mart: Learning Visual Action Trajectory Proposals for Manipulating 3D ARTiculated Objects, International Conference on Learning Representations (ICLR) 2022   
Chuanyu Pan*, Yanchao Yang*, Kaichun Mo, Yueqi Duan and Leonidas J. Guibas, Object Pursuit: Building a Space of Objects via Discriminative Weight Generation, International Conference on Learning Representations (ICLR) 2022   
Jie Yang*, Kaichun Mo*, Yu-Kun Lai, Leonidas J. Guibas and Lin Gao, DSG-Net: Learning Disentangled Structure and Geometry for 3D Shape Generation, ACM Transactions on Graphics (ToG) 2022 (presented at SIGGRAPH 2022)   
Kaichun Mo, Yuzhe Qin, Fanbo Xiang, Hao Su and Leonidas J. Guibas, O2O-Afford: Annotation-Free Large-Scale Object-Object Affordance Learning, Conference on Robot Learning (CoRL) 2021   
Shuo Cheng, Kaichun Mo and Lin Shao, Learning to Regrasp by Learning to Place, Conference on Robot Learning (CoRL) 2021   
Kaichun Mo, Leonidas J. Guibas, Mustafa Mukadam, Abhinav Gupta and Shubham Tulsiani, Where2Act: From Pixels to Actions for Articulated 3D Objects, International Conference on Computer Vision (ICCV) 2021   
Jialei Huang*, Guanqi Zhan*, Qingnan Fan, Kaichun Mo, Lin Shao, Baoquan Chen, Leonidas J. Guibas and Hao Dong, Generative 3D Part Assembly via Dynamic Graph Learning, NeurIPS 2020   
Kaichun Mo, He Wang, Xinchen Yan, and Leonidas J. Guibas, PT2PC: Learning to Generate 3D Point Cloud Shapes from Part Tree Conditions, European Conference on Computer Vision (ECCV) 2020   
Yichen Li*, Kaichun Mo*, Lin Shao, Minhyuk Sung, and Leonidas J. Guibas, Learning 3D Part Assembly from a Single Image, European Conference on Computer Vision (ECCV) 2020   
Fanbo Xiang, Yuzhe Qin, Kaichun Mo, Yikuan Xia, Hao Zhu, Fangchen Liu, Minghua Liu, Hanxiao Jiang, Yifu Yuan, He Wang, Li Yi, Angel X.Chang, Leonidas J. Guibas and Hao Su, SAPIEN: A SimulAted Part-based Interactive ENvironment. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Oral)   
Kaichun Mo*, Paul Guerrero*, Li Yi, Hao Su, Peter Wonka, Niloy Mitra, and Leonidas J. Guibas, StructEdit: Learning Structural Shape Variations, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020.   
Tiange Luo, Kaichun Mo, Zhiao Huang, Jiarui Xu, Siyu Hu, Liwei Wang and Hao Su, Learning to Group: A Bottom-Up Framework for 3D Part Discovery in Unseen Categories, ICLR 2020   
Kaichun Mo*, Paul Guerrero*, Li Yi, Hao Su, Peter Wonka, Niloy Mitra, and Leonidas J. Guibas, StructureNet: Hierarchical Graph Networks for 3D Shape Generation, Siggraph Asia 2019   
Kaichun Mo, Shilin Zhu, Angel Chang, Li Yi, Subarna Tripathi, Leonidas Guibas, and Hao Su, PartNet: A Large-scale Benchmark for Fine-grained and Hierarchical Part-level 3D Object Understanding, CVPR 2019   
Charles R. Qi, Hao Su, Kaichun Mo, and Leonidas J. Guibas. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017, Honolulu USA.