activity
20232026
most citedLearning the Geodesic Embedding with Graph Neural Networks

8 citations · 9 across the 7 of their papers we have counts for

collaborators

8 papers

cs.GR2026

HairCS: Reconstructing Strand-Based Hair from Hair Cards

Zixuan Lu, Tongtong Wang, Yuefan Shen +4

We present an automated pipeline that converts hair-card models into high-quality strand-based hairstyles. Given a collection of textured triangular or quad strips as input, our me…

cs.GR2026

Strand-based Hairstyle Generation via Large Reconstruction and Multimodal Models

Conghui Hao, Tao Huang, Yuefan Shen +3

Creating high-quality strand-based hairstyles in current production pipelines remains heavily dependent on skilled artists and time-consuming manual authoring, making it costly and…

cs.GR2026

HairLRM: Strand-based Hair Modeling via Large Reconstruction Models

Yuefan Shen, Yican Dong, Xiufeng Huang +3

The fundamental limitation of traditional strand-based modeling is not simply data scarcity, but the ill-posedness of inferring complex 3D fields from 2D imagery without structural…

cs.GR2025

Auto Hair Card Extraction for Smooth Hair with Differentiable Rendering

Zhongtian Zheng, Tao Huang, Haozhe Su +7

Hair cards remain a widely used representation for hair modeling in real-time applications, offering a practical trade-off between visual fidelity, memory usage, and performance. H…

cs.CV2024

Neural Laplacian Operator for 3D Point Clouds

Bo Pang, Zhongtian Zheng, Yilong Li +2

The discrete Laplacian operator holds a crucial role in 3D geometry processing, yet it is still challenging to define it on point clouds. Previous works mainly focused on construct…

cs.CV2024★ 1 cited

RPBG: Towards Robust Neural Point-based Graphics in the Wild

Qingtian Zhu, Zizhuang Wei, Zhongtian Zheng +5

Point-based representations have recently gained popularity in novel view synthesis, for their unique advantages, e.g., intuitive geometric representation, simple manipulation, and…