ARM: Appearance Reconstruction Model for Relightable 3D Generation

Xiang Feng, Chang Yu, Zoubin Bi, Yintong Shang, Feng Gao +4 more
2/9/2026

No official code found. Best implementation: arm-aigc/arm-aigc.github.io (1 star, MIT, last commit 2025-04-04).

Abstract

Recent image-to-ЗD reconstruction models have greatly advanced geometry generation, but they still struggle to faithfully generate realistic appearance. To address this, we introduce ARM, a novel method that reconstructs high-quality 3D meshes and realistic appearance from sparse-view images. The core of ARM lies in decoupling geometry from appearance, processing appearance within the UV texture space. Unlike previous methods, ARM improves texture quality by explicitly back-projecting measurements onto the texture map and processing them in a UV space module with a global receptive field. To resolve ambiguities between material and illumination in input images, ARM introduces a material prior that encodes semantic appearance information, enhancing the robustness of appearance decomposition. Trained on just 8 H100 GPUs, ARM outperforms existing methods both quantitatively and qualitatively. Our project page is available at https://arm-aigc.github.io.

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Code Implementations(1)

arm-aigc/arm-aigc.github.ioCommunity69%
MIT

ARM: Appearance Reconstruction Model for Relightable 3D Generation

10CSS, HTMLNov 14, 20241 years agoMIT

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Cite this paper

@article{feng2026arm,
  title  = {ARM: Appearance Reconstruction Model for Relightable 3D Generation},
  author = {Xiang Feng and Chang Yu and Zoubin Bi and Yintong Shang and Feng Gao and Hongzhi Wu and Kun Zhou and Chenfanfu Jiang and Yin Yang},
  year   = {2026},
  eprint = {2411.10825},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/2411.10825},
  journal = {CVPR 2024 2024}
}

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