8 citations · 9 across the 7 of their papers we have counts for
9 papers
HairFlow: Modeling 3D Hair Strands with Multi-Modal Conditioned Diffusion
Yating Wang, Kui Wu, Ran Yi +5
Modeling 3D hair strands is challenging due to complex hairstyle geometry, scarce high-quality training data, and incomplete or noisy input observations. Foundation models offer br…
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…
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…
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…
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…
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…
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…
Learning the Geodesic Embedding with Graph Neural Networks
Bo Pang, Zhongtian Zheng, Guoping Wang +1
We present GeGnn, a learning-based method for computing the approximate geodesic distance between two arbitrary points on discrete polyhedra surfaces with constant time complexity…
Visual-Guided Mesh Repair
Zhongtian Zheng, Xifeng Gao, Zherong Pan +4
Mesh repair is a long-standing challenge in computer graphics and related fields. Converting defective meshes into watertight manifold meshes can greatly benefit downstream applica…