FlashTex: Fast Relightable Mesh Texturing with LightControlNet

• URL: http://arxiv.org/abs/2402.13251v3
• Date: Thu, 17 Oct 2024 15:45:06 GMT
• Title: FlashTex: Fast Relightable Mesh Texturing with LightControlNet
• Authors: Kangle Deng, Timothy Omernick, Alexander Weiss, Deva Ramanan, Jun-Yan Zhu, Tinghui Zhou, Maneesh Agrawala,
• Abstract summary: We introduce LightControlNet, a new text-to-image model based on the ControlNet architecture. We apply our approach to disentangle material/reflectance in the resulting texture so that the mesh can be properlylit and rendered in any lighting environment.
• Score: 105.4683880648901
• License:
• Abstract: Manually creating textures for 3D meshes is time-consuming, even for expert visual content creators. We propose a fast approach for automatically texturing an input 3D mesh based on a user-provided text prompt. Importantly, our approach disentangles lighting from surface material/reflectance in the resulting texture so that the mesh can be properly relit and rendered in any lighting environment. We introduce LightControlNet, a new text-to-image model based on the ControlNet architecture, which allows the specification of the desired lighting as a conditioning image to the model. Our text-to-texture pipeline then constructs the texture in two stages. The first stage produces a sparse set of visually consistent reference views of the mesh using LightControlNet. The second stage applies a texture optimization based on Score Distillation Sampling (SDS) that works with LightControlNet to increase the texture quality while disentangling surface material from lighting. Our algorithm is significantly faster than previous text-to-texture methods, while producing high-quality and relightable textures.

Related papers

• TEXGen: a Generative Diffusion Model for Mesh Textures [63.43159148394021]
  We focus on the fundamental problem of learning in the UV texture space itself. We propose a scalable network architecture that interleaves convolutions on UV maps with attention layers on point clouds. We train a 700 million parameter diffusion model that can generate UV texture maps guided by text prompts and single-view images.
  arXiv  Detail & Related papers  (2024-11-22T05:22:11Z)
• Meta 3D TextureGen: Fast and Consistent Texture Generation for 3D Objects [54.80813150893719]
  We introduce Meta 3D TextureGen: a new feedforward method comprised of two sequential networks aimed at generating high-quality textures in less than 20 seconds. Our method state-of-the-art results in quality and speed by conditioning a text-to-image model on 3D semantics in 2D space and fusing them into a complete and high-resolution UV texture map. In addition, we introduce a texture enhancement network that is capable of up-scaling any texture by an arbitrary ratio, producing 4k pixel resolution textures.
  arXiv  Detail & Related papers  (2024-07-02T17:04:34Z)
• Infinite Texture: Text-guided High Resolution Diffusion Texture Synthesis [61.189479577198846]
  We present Infinite Texture, a method for generating arbitrarily large texture images from a text prompt. Our approach fine-tunes a diffusion model on a single texture, and learns to embed that statistical distribution in the output domain of the model. At generation time, our fine-tuned diffusion model is used through a score aggregation strategy to generate output texture images of arbitrary resolution on a single GPU.
  arXiv  Detail & Related papers  (2024-05-13T21:53:09Z)
• EucliDreamer: Fast and High-Quality Texturing for 3D Models with Depth-Conditioned Stable Diffusion [5.158983929861116]
  We present EucliDreamer, a simple and effective method to generate textures for 3D models given text and prompts. The texture is parametized as an implicit function on the 3D surface, which is optimized with the Score Distillation Sampling (SDS) process and differentiable rendering.
  arXiv  Detail & Related papers  (2024-04-16T04:44:16Z)
• TextureDreamer: Image-guided Texture Synthesis through Geometry-aware Diffusion [64.49276500129092]
  TextureDreamer is an image-guided texture synthesis method. It can transfer relightable textures from a small number of input images to target 3D shapes across arbitrary categories.
  arXiv  Detail & Related papers  (2024-01-17T18:55:49Z)
• Paint-it: Text-to-Texture Synthesis via Deep Convolutional Texture Map Optimization and Physically-Based Rendering [47.78392889256976]
  Paint-it is a text-driven high-fidelity texture map synthesis method for 3D rendering. Paint-it synthesizes texture maps from a text description by synthesis-through-optimization, exploiting the Score-Distillation Sampling (SDS) We show that DC-PBR inherently schedules the optimization curriculum according to texture frequency and naturally filters out the noisy signals from SDS.
  arXiv  Detail & Related papers  (2023-12-18T17:17:08Z)
• Consistent Mesh Diffusion [8.318075237885857]
  Given a 3D mesh with a UV parameterization, we introduce a novel approach to generating textures from text prompts. We demonstrate our approach on a dataset containing 30 meshes, taking approximately 5 minutes per mesh.
  arXiv  Detail & Related papers  (2023-12-01T23:25:14Z)
• TEXTure: Text-Guided Texturing of 3D Shapes [71.13116133846084]
  We present TEXTure, a novel method for text-guided editing, editing, and transfer of textures for 3D shapes. We define a trimap partitioning process that generates seamless 3D textures without requiring explicit surface textures.
  arXiv  Detail & Related papers  (2023-02-03T13:18:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.