“A Diffusion-Based Texturing Pipeline for Production-Grade Assets” by Lin, Smirnov and Smith – ACM SIGGRAPH HISTORY ARCHIVES

“A Diffusion-Based Texturing Pipeline for Production-Grade Assets” by Lin, Smirnov and Smith

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Conference:


Type(s):


Title:

    A Diffusion-Based Texturing Pipeline for Production-Grade Assets

Session/Category Title:   Generative AI and Style Transfer


Presenter(s)/Author(s):



Abstract:


    We introduce a pipeline for producing diverse high-quality textures for UV-unwrapped 3D assets via a Stable Diffusion Model. Our method is fast, controllable, and modular, allowing artists to work on production-grade assets and iterate quickly without making sacrifices to their standard workflows and tools.

References:


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    Yash Kant, Ziyi Wu, Michael Vasilkovsky, Guocheng Qian, Jian Ren, Riza Alp Guler, Bernard Ghanem, Sergey Tulyakov, Igor Gilitschenski, and Aliaksandr Siarohin. 2023. SPAD: Spatially Aware Multiview Diffusers. arXiv (2023).
    [3]
    Winnie Lin, Yilin Zhu, Demi Guo, and Ron Fedkiw. 2023. Leveraging Deepfakes to Close the Domain Gap between Real and Synthetic Images in Facial Capture Pipelines. arXiv:2204.10746 (2023).
    [4]
    Ben Poole, Ajay Jain, Jonathan T. Barron, and Ben Mildenhall. 2022. DreamFusion: Text-to-3D using 2D Diffusion. arXiv (2022).
    [5]
    Elad Richardson, Gal Metzer, Yuval Alaluf, Raja Giryes, and Daniel Cohen-Or. 2023. TEXTure: Text-Guided Texturing of 3D Shapes. In ACM SIGGRAPH 2023.
    [6]
    Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. 2021. High-Resolution Image Synthesis with Latent Diffusion Models.
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    [9]
    Xin Yu, Peng Dai, Wenbo Li, Lan Ma, Zhengzhe Liu, and Xiaojuan Qi. 2023. Texture generation on 3d meshes with point-uv diffusion. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 4206–4216.
    [10]
    Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. 2023. Adding Conditional Control to Text-to-Image Diffusion Models.


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