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Material Anything: Generating Materials for Any 3D Object via Diffusion
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We present Material Anything, a fully-automated, unified diffusion framework designed to generate physically-based materials for 3D objects. Unlike existing methods that rely on complex pipelines or case-specific optimizations, Material Anything offers a robust, end-to-end solution adaptable to objects under diverse lighting conditions. Our approach leverages a pre-trained image diffusion model, enhanced with a triple-head architecture and rendering loss to improve stability and material quality. Additionally, we introduce confidence masks as a dynamic switcher within the diffusion model, enabling it to effectively handle both textured and texture-less objects across varying lighting conditions. By employing a progressive material generation strategy guided by these confidence masks, along with a UV-space material refiner, our method ensures consistent, UV-ready material outputs. Extensive experiments demonstrate our approach outperforms existing methods across a wide range of object categories and lighting conditions.
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Cited by 1 Pith paper
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FMMC: Harnessing the Power of Foundation Models for Accurate Material Classification
Synthetic auto-labeled material images plus dual DINOv2–CLIP priors deliver large accuracy gains over prior material classifiers and zero-shot VLMs on real-world test sets.
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