Readme
Text2Tex
Text2Tex creates detailed textures for 3D meshes based on provided textual prompts. This approach integrates inpainting with a pre-trained depth-aware controlnet model, allowing for the gradual creation of high-resolution textures from various viewpoints.
See the paper , project page and original repository for more details.
How to Use the API
To generate textures with Text2Tex, you need to provide a mesh file (.obj) and enter a text description of the texture you would like to generate. The API input arguments are as follows:
- obj_file : 3D object file (.obj) you would like to generate texture for.
- prompt : text prompt to generate texture from.
- negative_prompt : use this to specify what you don’t want in the texture, helping to refine the results.
- ddim_steps : number of DDIM sampling steps, influencing the texture’s progression and detail.
- new_strength : percentage to determine the DDIM steps for generating new view.
- update_strength : percentage to determine the DDIM steps to update the view.
- num_viewpoints : number of different pre-determined viewpoints used to update the texture, more viewpoints result in consistent textures.
- viewpoint_mode : strategy for selecting viewpoints, either predefined or hemispherical.
- update_steps : number of iterations for texture updates, each enhancing quality or addressing specific texture artifacts.
- update_mode : the method by which texture updates are applied through the iterations.
- seed : seed for reproducibility, default value is None. Set to an arbitrary value for deterministic generation.
Usage Tips
The recommended pre-processing steps (as per authors) to get the best results are as follows:
- Y-axis is up.
- The mesh should face towards +Z.
- The mesh bounding box should be origin-aligned (note simply averaging the vertices coordinates could be problematic).
- The max length of the mesh bounding box should be around 1.
References
@article{chen2023text2tex,
title={Text2Tex: Text-driven Texture Synthesis via Diffusion Models},
author={Chen, Dave Zhenyu and Siddiqui, Yawar and Lee, Hsin-Ying and Tulyakov, Sergey and Nie{\ss}ner, Matthias},
journal={arXiv preprint arXiv:2303.11396},
year={2023},
}
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