title: UniTEX: Universal High Fidelity Generative Texturing for 3D Shapes
summary: We present UniTEX, a novel two-stage 3D texture generation framework to
  create high-quality, consistent textures for 3D assets. Existing approaches
  predominantly rely on UV-based inpainting to refine textures after reprojecting
  the generated multi-view images onto the 3D shapes, which introduces challenges
  related to topological ambiguity. To address this, we propose to bypass the
  limitations of UV mapping by operating directly in a unified 3D functional
  space. Specifically, we first propose that lifts texture generation into 3D
  space via Texture Functions (TFs)--a continuous, volumetric representation that
  maps any 3D point to a texture value based solely on surface proximity,
  independent of mesh topology. Then, we propose to predict these TFs directly
  from images and geometry inputs using a transformer-based Large Texturing Model
  (LTM). To further enhance texture quality and leverage powerful 2D priors, we
  develop an advanced LoRA-based strategy for efficiently adapting large-scale
  Diffusion Transformers (DiTs) for high-quality multi-view texture synthesis as
  our first stage. Extensive experiments demonstrate that UniTEX achieves
  superior visual quality and texture integrity compared to existing approaches,
  offering a generalizable and scalable solution for automated 3D texture
  generation. Code will available in: https://github.com/YixunLiang/UniTEX.
paper_authors._id: 6839325ddbf608133c740556
paper_authors.name: Yixun Liang
paper_authors._id: 6839325ddbf608133c740557
paper_authors.name: Kunming Luo
paper_authors._id: 6839325ddbf608133c740558
paper_authors.name: Xiao Chen
paper_authors._id: 6839325ddbf608133c740559
paper_authors.name: Rui Chen
paper_authors._id: 6839325ddbf608133c74055a
paper_authors.name: Hongyu Yan
paper_authors._id: 6839325ddbf608133c74055b
paper_authors.name: Weiyu Li
paper_authors._id: 6839325ddbf608133c74055c
paper_authors.name: Jiarui Liu
paper_authors._id: 6839325ddbf608133c74055d
paper_authors.name: Ping Tan
paper_title: UniTEX: Universal High Fidelity Generative Texturing for 3D Shapes
paper_summary: We present UniTEX, a novel two-stage 3D texture generation framework to
  create high-quality, consistent textures for 3D assets. Existing approaches
  predominantly rely on UV-based inpainting to refine textures after reprojecting
  the generated multi-view images onto the 3D shapes, which introduces challenges
  related to topological ambiguity. To address this, we propose to bypass the
  limitations of UV mapping by operating directly in a unified 3D functional
  space. Specifically, we first propose that lifts texture generation into 3D
  space via Texture Functions (TFs)--a continuous, volumetric representation that
  maps any 3D point to a texture value based solely on surface proximity,
  independent of mesh topology. Then, we propose to predict these TFs directly
  from images and geometry inputs using a transformer-based Large Texturing Model
  (LTM). To further enhance texture quality and leverage powerful 2D priors, we
  develop an advanced LoRA-based strategy for efficiently adapting large-scale
  Diffusion Transformers (DiTs) for high-quality multi-view texture synthesis as
  our first stage. Extensive experiments demonstrate that UniTEX achieves
  superior visual quality and texture integrity compared to existing approaches,
  offering a generalizable and scalable solution for automated 3D texture
  generation. Code will available in: https://github.com/YixunLiang/UniTEX.
paper_ai_summary: UniTEX generates high-quality, consistent 3D textures by using Texture Functions and adapting Diffusion Transformers directly from images and geometry without UV mapping.
paper_ai_keywords: Texture Functions transformer-based Large Texturing Model LoRA Diffusion Transformers