Material Anything: Generating Materials for Any 3D Object via Diffusion

November 22, 2024 Β· Declared Dead Β· πŸ› Computer Vision and Pattern Recognition

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Authors Xin Huang, Tengfei Wang, Ziwei Liu, Qing Wang arXiv ID 2411.15138 Category cs.CV: Computer Vision Cross-listed cs.GR Citations 23 Venue Computer Vision and Pattern Recognition Last Checked 4 months ago
Abstract
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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