用稀疏图像生成高保真3D模型和真实材质，解耦几何与外观处理。
ARM: Appearance Reconstruction Model for Relightable 3D Generation
- 将外观重建从三维空间转到UV纹理空间，提升细节质量。
- 通过材质先验解决光照与材质混淆问题，提升分解鲁棒性。
- 仅用8张H100 GPU训练，性能超越现有方法。
近期的图像到3D重建模型在几何生成方面取得了显著进展，但仍难以忠实生成真实外观。为此，我们提出ARM——一种从稀疏视角图像重建高质量3D网格和真实外观的新方法。ARM的核心是将几何与外观解耦，在UV纹理空间中处理外观。不同于以往方法，ARM通过显式反投影测量到纹理图，并在具有全局感受野的UV空间模块中处理，从而提升纹理质量。为解决输入图像中材质与光照之间的模糊性，ARM引入材质先验，编码语义外观信息，增强外观分解的鲁棒性。仅使用8张H100 GPU训练，ARM在定量和定性上均优于现有方法。
原文摘要 · Abstract (English)
Recent image-to-3D reconstruction models have greatly advanced geometry generation, but they still struggle to faithfully generate realistic appearance. To address this, we introduce ARM, a novel method that reconstructs high-quality 3D meshes and realistic appearance from sparse-view images. The core of ARM lies in decoupling geometry from appearance, processing appearance within the UV texture space. Unlike previous methods, ARM improves texture quality by explicitly back-projecting measurements onto the texture map and processing them in a UV space module with a global receptive field. To resolve ambiguities between material and illumination in input images, ARM introduces a material prior that encodes semantic appearance information, enhancing the robustness of appearance decomposition. Trained on just 8 H100 GPUs, ARM outperforms existing methods both quantitatively and qualitatively.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。