GenVDM: Generating Vector Displacement Maps From a Single Image

Yuezhi Yang, Qimin Chen, Vladimir G. Kim, S. Chaudhuri, Qi-Xing Huang +1 more
2/10/2026

No official code found. Best implementation: yyuezhi/GenVDM (33 stars, Apache-2.0, last commit 2025-07-06).

Abstract

We introduce the first method for generating Vector Displacement Maps (VDMs): parameterized, detailed geometric stamps commonly used in 3D modeling. Given a single input image, our method first generates multi-view normal maps and then reconstructs a VDM from the normals via a novel reconstruction pipeline. We also propose an efficient algorithm for extracting VDMs from 3D objects, and present the first academic VDM dataset. Compared to existing 3D generative models focusing on complete shapes, we focus on generating parts that can be seamlessly attached to shape surfaces. The method gives artists rich control over adding geometric details to a 3D shape. Experiments demonstrate that our approach outperforms existing baselines. Generating VDMs offers additional benefits, such as using 2D image editing to customize and refine 3D details. Code and data are available at https://yyuezhi.github.io/GenVDM/.

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Code Implementations(1)

yyuezhi/GenVDMCommunity65%
Apache-2.0

Code for [CVPR 2025] GenVDM:Generating Vector Displacement Maps From a Single Image

332Shell, CythonFeb 14, 20251 years agoApache-2.0

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Cite this paper

@article{yang2026genvdm,
  title  = {GenVDM: Generating Vector Displacement Maps From a Single Image},
  author = {Yuezhi Yang and Qimin Chen and Vladimir G. Kim and S. Chaudhuri and Qi-Xing Huang and Zhiqin Chen},
  year   = {2026},
  eprint = {2503.00605},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/2503.00605},
  journal = {CVPR 2025 2025}
}

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