CustomTex: High-fidelity Indoor Scene Texturing via Multi-Reference Customization
Weilin Chen, Jiahao Rao, Wenhao Wang, Xinyang Li, Xuan Cheng, Liujuan Cao

TL;DR
CustomTex is a novel framework that enables high-fidelity, instance-level indoor scene texturing driven by reference images, overcoming limitations of previous text-driven methods in quality and control.
Contribution
We introduce CustomTex, a dual-distillation based method that separates semantic control from pixel-level enhancement for precise, high-quality 3D scene texturing from reference images.
Findings
Achieves precise instance-level consistency with reference images.
Produces textures with superior sharpness and fewer artifacts.
Outperforms state-of-the-art methods in visual fidelity.
Abstract
The creation of high-fidelity, customizable 3D indoor scene textures remains a significant challenge. While text-driven methods offer flexibility, they lack the precision for fine-grained, instance-level control, and often produce textures with insufficient quality, artifacts, and baked-in shading. To overcome these limitations, we introduce CustomTex, a novel framework for instance-level, high-fidelity scene texturing driven by reference images. CustomTex takes an untextured 3D scene and a set of reference images specifying the desired appearance for each object instance, and generates a unified, high-resolution texture map. The core of our method is a dual-distillation approach that separates semantic control from pixel-level enhancement. We employ semantic-level distillation, equipped with an instance cross-attention, to ensure semantic plausibility and ``reference-instance''…
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · 3D Shape Modeling and Analysis · Computer Graphics and Visualization Techniques
