most citedGenerative 3D Part Assembly via Part-Whole-Hierarchy Message Passing

1 citations · 1 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CV2026

Motion Beyond Morphology: Bootstrapping Cross-Category Motion Transfer from Abstract Motion Representations

Zhixue Fang, Zhimin Zhang, Bi'an Du +6

Video motion transfer aims to animate a target object using dynamics from a reference video. Existing formulations largely rely on fixed structural correspondence, which becomes il…

cs.CV2026

MOC-3D: Manifold-Order Consistency for Text-to-3D Generation

Chenyang Fan, Junshi Cheng, Wen Yang +5

With the burgeoning development of fields such as the Metaverse, Virtual Reality (VR), and Digital Twins, text-to-3D generation has emerged as a research hotspot in both academia a…

cs.CV2026

View-Consistent 3D Scene Editing via Dual-Path Structural Correspondense and Semantic Continuity

Pufan Li, Bi'an Du, Shenghe Zheng +2

Text-driven 3D scene editing has recently attracted increasing attention. Most existing methods follow a render-edit-optimize pipeline, where multi-view images are rendered from a…

cs.CV2026

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

Bi'an Du, Daizong Liu, Pufan Li +1

Single-image 3D generation lies at the core of vision-to-graphics models in the real world. However, it remains a fundamental challenge to achieve reliable generalization across di…

cs.CV2024★ 1 cited

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

Bi'an Du, Xiang Gao, Wei Hu +1

Generative 3D part assembly involves understanding part relationships and predicting their 6-DoF poses for assembling a realistic 3D shape. Prior work often focus on the geometry o…

cs.CV2024

RangeLDM: Fast Realistic LiDAR Point Cloud Generation

Qianjiang Hu, Zhimin Zhang, Wei Hu

Autonomous driving demands high-quality LiDAR data, yet the cost of physical LiDAR sensors presents a significant scaling-up challenge. While recent efforts have explored deep gene…

cs.CV2024

InvariantOODG: Learning Invariant Features of Point Clouds for Out-of-Distribution Generalization

Zhimin Zhang, Xiang Gao, Wei Hu

The convenience of 3D sensors has led to an increase in the use of 3D point clouds in various applications. However, the differences in acquisition devices or scenarios lead to div…