title: Material Anything: Generating Materials for Any 3D Object via Diffusion
summary: 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.
paper_authors._id: 674455fdde9997dd26a9ed4b
paper_authors.name: Xin Huang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-11-25T20:38:54.950Z
paper_authors._id: 638d7d33c8912be69c1a5849
paper_authors.avatarUrl: /avatars/475efc364e10f3594467bd45260d8999.svg
paper_authors.fullname: Xin Huang
paper_authors.type: user
paper_authors.user: xanderhuang
paper_authors._id: 674455fdde9997dd26a9ed4c
paper_authors.name: Tengfei Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-26T21:26:00.088Z
paper_authors._id: 62e7c26236a8e8a827ff0891
paper_authors.avatarUrl: /avatars/15648407233bf5757262dbc04464d02e.svg
paper_authors.fullname: Tengfei Wang
paper_authors.type: user
paper_authors.user: tfwang
paper_authors._id: 674455fdde9997dd26a9ed4d
paper_authors.name: Ziwei Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-26T21:25:43.153Z
paper_authors._id: 62ab1ac1d48b4d8b048a3473
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1656826685333-62ab1ac1d48b4d8b048a3473.png
paper_authors.fullname: Ziwei Liu
paper_authors.type: user
paper_authors.user: liuziwei7
paper_authors._id: 674455fdde9997dd26a9ed4e
paper_authors.name: Qing Wang
paper_title: Material Anything: Generating Materials for Any 3D Object via Diffusion
paper_summary: 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.
paper_ai_summary: Material Anything is a unified diffusion framework that generates physically-based materials for 3D objects using a pre-trained image diffusion model with enhancements for stability and quality.
paper_ai_keywords: diffusion framework physically-based materials pre-trained image diffusion model triple-head architecture rendering loss confidence masks progressive material generation UV-space material refiner