MatPedia：面向高保真材质合成的通用生成式基础模型
MatPedia: A Universal Generative Foundation for High-Fidelity Material Synthesis
把 RGB 外观与四张 PBR 贴图拼成 5 通道序列、直接复用视频扩散骨干来建模跨通道相关性，这一「多通道序列化 + 视频先验迁移」的做法可迁移到任何需要同时生成多张相关图像的任务（如 albedo/normal/depth 联合预测、多光照分解）。
原文依据 By formulating them as a 5-channel sequence and employing video diffusion architectures
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论基于物理的渲染（PBR）材质是照片级真实感图形学的基础，但其制作仍然劳动密集且需要专业知识。尽管生成模型已推动了材质合成的发展，现有方法仍缺乏一种能够桥接自然图像外观与 PBR 属性的统一表示，导致任务专用流程彼此割裂，且无法利用大规模 RGB 图像数据。我们提出 MatPedia，一个建立在新型联合 RGB-PBR 表示之上的基础模型，该表示将材质紧凑地编码为两个相互依赖的潜变量：一个对应 RGB 外观，另一个对应编码互补物理属性的四张 PBR 贴图。通过将它们构造成 5 通道序列并采用视频扩散架构，MatPedia 在从 RGB 生成模型迁移视觉先验的同时，自然地捕捉了它们之间的相关性。这一联合表示使得单一架构即可统一处理多种材质任务——文本到材质生成、图像到材质生成以及本征分解。MatPedia 在 MatHybrid-410K（一个将 PBR 数据集与大规模 RGB 图像相结合的混合语料）上训练，实现了原生 1024x1024 合成，在质量与多样性上均大幅超越现有方法。
英文原文摘要 1297 字符
Physically-based rendering (PBR) materials are fundamental to photorealistic graphics, yet their creation remains labor-intensive and requires specialized expertise. While generative models have advanced material synthesis, existing methods lack a unified representation bridging natural image appearance and PBR properties, leading to fragmented task-specific pipelines and inability to leverage large-scale RGB image data. We present MatPedia, a foundation model built upon a novel joint RGB-PBR representation that compactly encodes materials into two interdependent latents: one for RGB appearance and one for the four PBR maps encoding complementary physical properties. By formulating them as a 5-channel sequence and employing video diffusion architectures, MatPedia naturally captures their correlations while transferring visual priors from RGB generation models. This joint representation enables a unified framework handling multiple material tasks--text-to-material generation, image-to-material generation, and intrinsic decomposition--within a single architecture. Trained on MatHybrid-410K, a mixed corpus combining PBR datasets with large-scale RGB images, MatPedia achieves native 1024x1024 synthesis that substantially surpasses existing approaches in both quality and diversity.