I am an assistant professor at Department of Computer Science, Harbin Institute of Technology, Shenzhen.

I obtained my Ph.D. degree in 2025 from the Technical University of Munich (TUM), Germany, under the supervison of Dr. Federico Tombari. In 2020, I received my master degree at Department of Automation in Tsinghua University, under the supervision of Prof. Xiangyang Ji. In 2017, I received the B.Eng. degree at Department of Automation in Tsinghua University.

Research

My research topic is human-object pose estimation, generation and its applications in robotics. Feel free to reach me at diyan@hit.edu.cn

News

• [2024/02 ] 5 papers are accepted to CVPR2024. KP-RED and ShapeMaker focus on joint shape canonicalization, segmentation, retrieval and deformation. HiPose achieves nearly SOTA performance on instance-level pose estimation, whilst running super fast. SecondPose outperforms competitors on category-level pose estimation. MOHO uses a synthetic-to-real strategy for hand-held object reconstruction, and provides a new synthetic dataset for training.
• [2024/01 ] Our paper SG-Bot on scene-graph-based object rearrangement is accepted to ICRA2024.
• [2023/09 ] Our paper DDF-HO on hand-held object reconstruction is accepted to NeurIPS2023.
• [2023/09 ] Our paper CommonScenes on scene generation from scene graph is accepted to NeurIPS2023.
• [2023/07 ] Our paper U-RED on unsupervised shape retrieval and deformation in indoor scenes is accepted to ICCV2023.
• [2023/03 ] Our paper SST on neural reconstruction from RGB sequences is accepted to ICME2023.
• [2023/02 ] Our paper IPCC-TP on trajectory prediction in traffic scenes is accepted to CVPR2023.
• [2023/02 ] Our paper on self-supervised category-level pose estimation is accepted to RAL2023.
• [2023/01 ] Our robotic grasping paper MonoGraspNet is accepted to ICRA2023.
• [2023/01 ] Our 3D object detection method (category-level pose estimation in traffic scenes) OPA-3D is accepted to the IEEE Robotics and Automation Letters (RAL2023).
• [2022/10 ] Our method ZebraPoseSAT won the ‘Overall Best Segmentation Method’, ‘Best BlenderProc-Trained Segmentation Method’ in BOP Challenge, ECCV 2022. Our method is also the second best RGB-Only Pose Estimation method. I contributed part of the code.
• [2022/06 ] Our category-level pose estimation works GPV-Pose, RBP-Pose, SSP-Pose are accepted to CVPR2022, ECCV2022, IROS2022 respectively.
• [2021/06 ] Our instance-level pose estimation work SO-Pose is accepted at ICCV2021.
• [2020/06 ] Our dynamic reconstruction works are accepted at ICCV2019, ICRA2020 respectively.