TEXTure: Semantic Texture Transfer using Text Tokens

teaser.mp4

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Abstract In this paper, we present TEXTure, a novel method for text-guided generation, editing, and transfer of textures for 3D shapes. Leveraging a pretrained depth-to-image diffusion model, TEXTure applies an iterative scheme that paints a 3D model from different viewpoints. Yet, while depth-to-image models can create plausible textures from a single viewpoint, the stochastic nature of the generation process can cause many inconsistencies when texturing an entire 3D object. To tackle these problems, we dynamically define a trimap partitioning of the rendered image into three progression states, and present a novel elaborated diffusion sampling process that uses this trimap representation to generate seamless textures from different views. We then show that one can transfer the generated texture maps to new 3D geometries without requiring explicit surface-to-surface mapping, as well as extract semantic textures from a set of images without requiring any explicit reconstruction. Finally, we show that TEXTure can be used to not only generate new textures but also edit and refine existing textures using either a text prompt or user-provided scribbles. We demonstrate that our TEXTuring method excels at generating, transferring, and editing textures through extensive evaluation, and further close the gap between 2D image generation and 3D texturing.

Description 📜

Official Implementation for "TEXTure: Semantic Texture Transfer using Text Tokens".

TL;DR - TEXTure takes an input mesh and a conditioning text prompt and paints the mesh with high-quality textures, using an iterative diffusion-based process. In the paper we show that TEXTure can be used to not only generate new textures but also edit and refine existing textures using either a text prompt or user-provided scribbles.

Recent Updates 📰

• Feb 06 2023 - Code release

Getting Started

Installation 💾

Install the common dependencies from the requirements.txt file

pip install -r requirements.txt

and Kaolin

pip install kaolin==0.11.0 -f https://nvidia-kaolin.s3.us-east-2.amazonaws.com/{TORCH_VER}_{CUDA_VER}.html

Note that you also need a 🤗 token for StableDiffusion. First accept conditions for the model you want to use, default one is stabilityai/stable-diffusion-2-depth. Then, add a TOKEN file access token to the root folder of this project, or use the huggingface-cli login command

Running

Text Conditioned Texture Generation

Try out painting the Napoleon from Three D Scans with a text prompt

python -m scripts.run_texture --config_path=configs/text_guided/napoleon.yaml

Or a next gen nascar from ModelNet40

python -m scripts.run_texture --config_path=configs/text_guided/nascar.yaml

Texture Transfer from Meshes

Documentation coming soon

Texture Transfer from Images

Documentation coming soon

Texture Refinement

Documentation coming soon

Texture Editing

Documentation coming soon