Bi'an Du (杜毕安)

Biography

Bi'an Du is a fourth-year Ph.D. student at the Wangxuan Institute of Computer Technology, Peking University, advised by Prof. Wei Hu. He received his bachelor's degree from Peking University in 2022, where he studied in the Turing Class. His research focuses on Computer Vision, with primary interests in 3D/4D generation and reconstruction, 3D understanding, and world modeling 🌍. More broadly, he is interested in building intelligent systems that can generate, perceive, understand, render, and iteratively refine realistic 3D worlds in a scalable and interactive manner, with the long-term goal of aligning such systems more closely with human cognition and physical understanding of the real world. He has also worked on related topics including adversarial robustness, LVLM, video generation, and motion generation. He welcomes academic discussions, research collaborations, and related opportunities. 🤝

News

• [2026-03]: 1 ICME paper accepted
• [2025-07]: 1 ACM MM paper accepted
• [2024-09]: Bi'an Du receives the National Scholarship
• [2024-04]: 1 CVPR paper accepted
• [2023-03]: 1 TPAMI paper accepted
• [2022-06]: Bi'an Du receives "Excellent Undergraduate" both in Peking University & Beijing
• [2022-06]: Bi'an Du receives the Top-10 Excellent Undergraduate Thesis Award in EECS, Peking University
• [2021-07]: 1 ACM MM paper accepted

Selected Papers

ICME 2026

• From Part to Whole: 3D Generative World Model with an Adaptive Structural Hierarchy

  Bi'an Du^*, Daizong Liu^*, Pufan Li, Wei Hu
  IEEE International Conference on Multimedia and Expo (ICME), 2026
  [Paper]
• TL;DR: This work proposes a part-to-whole 3D generative world model that learns an adaptive structural hierarchy from a single image. Through slot-gating and prototype-based part sharing, it improves cross-category generalization, structural consistency, and part-count extrapolation under sparse supervision.

ACM MM 2025

• Behave Your Motion: Habit-preserved Cross-category Animal Motion Transfer

  Zhimin Zhang^*, Bi'an Du^*, Caoyuan Ma, Zheng Wang, Wei Hu
  Proceedings of the 33rd ACM International Conference on Multimedia (ACM MM 2025)
  [Paper]
• TL;DR: This work explores cross-category animal motion transfer with an emphasis on preserving species-specific motion habits, enabling transferred motions that remain natural and behavior-consistent for the target category.

CVPR 2024

• Generative 3D Part Assembly via Part-Whole-Hierarchy Message Passing

  Bi'an Du, Xiang Gao, Wei Hu, Renjie Liao
  IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024
  [Paper] [Code]
• TL;DR: This work introduces a part-whole-hierarchy message passing framework for generative 3D part assembly. By first estimating latent super-part poses and then refining part-level poses with hierarchical interactions, it improves assembly accuracy, connectivity, and interpretability on PartNet.

TPAMI 2023

• Deep Point Set Resampling via Gradient Fields

  Haolan Chen*, Bi'an Du*, Shitong Luo, Wei Hu
  IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2023
  [Paper] [Code]
• TL;DR: This work formulates point cloud restoration as gradient-field learning and performs iterative gradient ascent to move degraded samples toward clean underlying surfaces. A continuous gradient prior enables strong denoising and upsampling performance across noisy and sparse point cloud settings.

ACM MM 2021

• Self-Contrastive Learning with Hard Negative Sampling for Self-supervised Point Cloud Learning

  Bi'an Du, Xiang Gao, Wei Hu, Xin Li
  Proceedings of the 29th ACM International Conference on Multimedia (ACM MM 2021)
  [Paper] [Code]
• TL;DR: This work proposes a self-contrastive learning framework with hard negative sampling for self-supervised point cloud representation learning. By mining informative negative pairs and jointly optimizing self-similarity and contrastive objectives, SCL learns stronger 3D features and improves transfer performance on downstream classification and segmentation tasks.