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Material Anything: Generating Materials for Any 3D Object via Diffusion

  • Xin Huang
  • , Tengfei Wang
  • , Ziwei Liu
  • , Qing Wang
  • Northwestern Polytechnical University Xian
  • Shanghai Artificial Intelligence Laboratory
  • Nanyang Technological University

Research output: Contribution to journalConference articlepeer-review

9 Scopus citations

Abstract

We present Material Anything, a fully-automated, unified diffusion framework designed to generate physically-based materials for 3D objects. Unlike existing methods that rely on complex pipelines or case-specific optimizations, Material Anything offers a robust, end-to-end solution adaptable to objects under diverse lighting conditions. Our approach leverages a pre-trained image diffusion model, enhanced with a triple-head architecture and rendering loss to improve stability and material quality. Additionally, we introduce confidence masks as a dynamic switcher within the diffusion model, enabling it to effectively handle both textured and texture-less objects across varying lighting conditions. By employing a progressive material generation strategy guided by these confidence masks, along with a UV-space material refiner, our method ensures consistent, UV-ready material outputs. Extensive experiments demonstrate our approach outperforms existing methods across a wide range of object categories and lighting conditions.

Original languageEnglish
Pages (from-to)26556-26565
Number of pages10
JournalProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
DOIs
StatePublished - 2025
Event2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2025 - Nashville, United States
Duration: 11 Jun 202515 Jun 2025

Keywords

  • diffusion model
  • material estimation
  • material refinement
  • progressive generation

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