Paint-it: Text-to-Texture Synthesis via Deep Convolutional Texture Map Optimization and Physically-Based Rendering
Kim Youwang, Tae-Hyun Oh, Gerard Pons-Moll

TL;DR
Paint-it introduces a novel text-driven texture synthesis method for 3D meshes that leverages a new neural parameterization and optimization technique, enabling high-quality, physically-based textures within minutes.
Contribution
The paper proposes Deep Convolutional Physically-Based Rendering (DC-PBR) parameterization and a texture optimization approach that improves texture quality and efficiency in text-to-texture synthesis.
Findings
Achieves high-quality PBR textures in 15 minutes from text descriptions.
Demonstrates generalization to large-scale mesh datasets.
Enables test-time applications like relighting and material control.
Abstract
We present Paint-it, a text-driven high-fidelity texture map synthesis method for 3D meshes via neural re-parameterized texture optimization. Paint-it synthesizes texture maps from a text description by synthesis-through-optimization, exploiting the Score-Distillation Sampling (SDS). We observe that directly applying SDS yields undesirable texture quality due to its noisy gradients. We reveal the importance of texture parameterization when using SDS. Specifically, we propose Deep Convolutional Physically-Based Rendering (DC-PBR) parameterization, which re-parameterizes the physically-based rendering (PBR) texture maps with randomly initialized convolution-based neural kernels, instead of a standard pixel-based parameterization. We show that DC-PBR inherently schedules the optimization curriculum according to texture frequency and naturally filters out the noisy signals from SDS. In…
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Taxonomy
TopicsComputer Graphics and Visualization Techniques · 3D Shape Modeling and Analysis · Generative Adversarial Networks and Image Synthesis
