Mingdong Wu | 吴铭东

2024

1. Arxiv 2024

   UniDexFPM: Universal Dexterous Functional Pre-grasp Manipulation Via Diffusion Policy

   Tianhao Wu*, Yunchong Gan*, Mingdong Wu, Jingbo Cheng, Yaodong Yang, Yixin Zhu, and Hao Dong

   Under Review, 2024

   Bib PDF Website

   @article{zhang2024unidexfpm,
     title = {UniDexFPM: Universal Dexterous Functional Pre-grasp Manipulation Via Diffusion Policy},
     author = {Wu*, Tianhao and Gan*, Yunchong and Wu, Mingdong and Cheng, Jingbo and Yang, Yaodong and Zhu, Yixin and Dong, Hao},
     journal = {Under Review},
     year = {2024}
   }

   
2. ECCV 2024

   Omni6DPose: A Benchmark and Model for Universal 6D Object Pose Estimation and Tracking

   Jiyao Zhang*, Weiyao Huang*, Bo Peng*, Mingdong Wu, Fei Hu, Zijian Chen, Bo Zhao, and Hao Dong

   European Conference on Computer Vision, 2024

   Bib PDF Website

   @article{zhang2024omni6dpose,
     title = {Omni6DPose: A Benchmark and Model for Universal 6D Object Pose Estimation and Tracking},
     author = {Zhang*, Jiyao and Huang*, Weiyao and Peng*, Bo and Wu, Mingdong and Hu, Fei and Chen, Zijian and Zhao, Bo and Dong, Hao},
     journal = {European Conference on Computer Vision},
     year = {2024}
   }

   
3. RAL 2024

   Distilling Functional Rearrangement Priors from Large Models

   Mingdong Wu*, Yiming Zeng*, Long Yang, Jiyao Zhang, Hao Ding, Hui Cheng, and Hao Dong

   IEEE Robotics and Automation Letters, 2024

   Bib PDF Website

   @article{zeng2023distilling,
     title = {Distilling Functional Rearrangement Priors from Large Models},
     author = {Wu*, Mingdong and Zeng*, Yiming and Yang, Long and Zhang, Jiyao and Ding, Hao and Cheng, Hui and Dong, Hao},
     journal = {IEEE Robotics and Automation Letters},
     year = {2024}
   }

   

2023

1. NeurIPS 2023

   GenPose: Generative Category-level Object Pose Estimation via Diffusion Models

   Jiyao Zhang*, Mingdong Wu*, and Hao Dong

   Thirty-seventh Conference on Neural Information Processing Systems, 2023

   Abs Bib PDF Website Code 机器之心

   We explore a pure generative approach to tackle the multi-hypothesis issue in 6D Category-level Object Pose Estimation. The key idea is to generate pose candidates using a score-based diffusion model and filter out outliers using an energy-based diffusion model. By aggregating the remaining candidates, we can obtain a robust and high-quality output pose.

   @article{zhang2023genpose,
     news = {机器之心},
     news_link = {https://mp.weixin.qq.com/s/RYV_aap9eYtwX_4_Ghr5Vw},
     sota_link = {https://paperswithcode.com/sota/6d-pose-estimation-using-rgbd-on-real275?p=genpose-generative-category-level-object-pose},
     sota_badge = {https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/genpose-generative-category-level-object-pose/6d-pose-estimation-using-rgbd-on-real275},
     star = {https://img.shields.io/github/stars/Jiyao06/GenPose?style=social&label=Code+Stars},
     title = {GenPose: Generative Category-level Object Pose Estimation via Diffusion Models},
     author = {Zhang*, Jiyao and Wu*, Mingdong and Dong, Hao},
     journal = {Thirty-seventh Conference on Neural Information Processing Systems},
     year = {2023}
   }

   
2. NeurIPS 2023

   Learning Score-based Grasping Primitive for Human-assisting Dexterous Grasping

   Tianhao Wu*, Mingdong Wu*, Jiyao Zhang, Yunchong Gan, and Hao Dong

   Thirty-seventh Conference on Neural Information Processing Systems, 2023

   Bib PDF Website Code 新智元

   @article{wu2023learning,
     news = {新智元},
     news_link = {https://mp.weixin.qq.com/s/hpzZWMizR8tPSGIvGVjPoA},
     star = {https://img.shields.io/github/stars/tianhaowuhz/human-assisting-dex-grasp?style=social&label=Code+Stars},
     title = {Learning Score-based Grasping Primitive for Human-assisting Dexterous Grasping},
     author = {Wu*, Tianhao and Wu*, Mingdong and Zhang, Jiyao and Gan, Yunchong and Dong, Hao},
     journal = {Thirty-seventh Conference on Neural Information Processing Systems},
     year = {2023}
   }

   
3. SIGGRAPH Asia
   2023

   Learning Gradient Fields for Scalable and Generalizable Irregular Packing

   Tianyang Xue*, Mingdong Wu*, Lin Lu, Haoxuan Wang, Hao Dong, and Baoquan Chen

   SIGGRAPH Asia, 2023

   Bib PDF Website

   @article{Xue2023learning,
     abbryear = {2023},
     title = {Learning Gradient Fields for Scalable and Generalizable Irregular Packing},
     author = {Xue*, Tianyang and Wu*, Mingdong and Lu, Lin and Wang, Haoxuan and Dong, Hao and Chen, Baoquan},
     journal = {SIGGRAPH Asia},
     year = {2023}
   }

   
4. BMVC 2023
   Oral

   Score-PA: Score-based 3D Part Assembly

   Junfeng Cheng, Mingdong Wu, Ruiyuan Zhang, Guanqi Zhan, Chao Wu, and Hao Dong

   British Machine Vision Conference, 2023

   Bib PDFCode

   @article{cheng2023score,
     oral = {Oral},
     title = {Score-PA: Score-based 3D Part Assembly},
     author = {Cheng, Junfeng and Wu, Mingdong and Zhang, Ruiyuan and Zhan, Guanqi and Wu, Chao and Dong, Hao},
     journal = {British Machine Vision Conference},
     year = {2023}
   }

   
5. CVPR 2023

   GFPose: Learning 3d human pose prior with gradient fields

   Hai Ci, Mingdong Wu, Wentao Zhu, Xiaoxuan Ma, Hao Dong, Fangwei Zhong, and Yizhou Wang

   Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

   Abs Bib PDF Website Code

   GFPose is a unified 3D human pose prior model that can be easily used for various applications, e.g., 3D human pose estimation, pose denoising and generation. Our key idea is to estimate the gradient field (a.k.a, score) of the perturbed human pose. We can leverage the gradient to adjust poses to be more plausible and feasible to a task specification.

   @inproceedings{ci2023gfpose,
     sota_link = {https://paperswithcode.com/sota/multi-hypotheses-3d-human-pose-estimation-on?p=gfpose-learning-3d-human-pose-prior-with},
     sota_badge = {https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/gfpose-learning-3d-human-pose-prior-with/multi-hypotheses-3d-human-pose-estimation-on},
     star = {https://img.shields.io/github/stars/Embracing/GFPose?style=social&label=Code+Stars},
     title = {GFPose: Learning 3d human pose prior with gradient fields},
     author = {Ci, Hai and Wu, Mingdong and Zhu, Wentao and Ma, Xiaoxuan and Dong, Hao and Zhong, Fangwei and Wang, Yizhou},
     booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
     pages = {4800--4810},
     year = {2023}
   }

   

2022

1. NeurIPS 2022

   TarGF: Learning Target Gradient Field to Rearrange Objects without Explicit Goal Specification

   Mingdong Wu*, Fangwei Zhong*, Yulong Xia, and Hao Dong

   Advances in Neural Information Processing Systems, 2022

   Abs Bib PDF WebsiteCode

   We study object rearrangement without explicit goal specification. The agent is given examples from a target distribution and aims at rearranging objects to increase the likelihood of the distribution. Our key idea is to learn a target gradient field that indicates the fastest direction to increase the likelihood from examples via score-matching.

   @inproceedings{wu2022targf,
     title = {Tar{GF}: Learning Target Gradient Field to Rearrange Objects without Explicit Goal Specification},
     author = {Wu*, Mingdong and Zhong*, Fangwei and Xia, Yulong and Dong, Hao},
     booktitle = {Advances in Neural Information Processing Systems},
     editor = {Oh, Alice H. and Agarwal, Alekh and Belgrave, Danielle and Cho, Kyunghyun},
     year = {2022},
     url = {https://openreview.net/forum?id=Euv1nXN98P3}
   }