Physically realistic materials are pivotal in augmenting the realism of 3D assets across various applications and lighting conditions. However, existing 3D assets and generative models often lack authentic material properties. Manual assignment of materials using graphic software is a tedious and time-consuming task. In this paper, we exploit advancements in Multimodal Large Language Models (MLLMs), particularly GPT-4V, to present a novel approach, Make-it-Real: 1) We demonstrate that GPT-4V can effectively recognize and describe materials, allowing the construction of a detailed material library. 2) Utilizing a combination of visual cues and hierarchical text prompts, GPT-4V precisely identifies and aligns materials with the corresponding components of 3D objects. 3) The correctly matched materials are then meticulously applied as reference for the new SVBRDF material generation according to the original diffuse map, significantly enhancing their visual authenticity. Make-it-Real offers a streamlined integration into the 3D content creation workflow, showcasing its utility as an essential tool for developers of 3D assets.

翻译：物理逼真的材质对于增强3D资产在各种应用场景及光照条件下的真实感至关重要。然而，现有3D资产与生成模型往往缺乏真实的材质属性。通过图形软件手动分配材质是一项繁琐且耗时的任务。本文利用多模态大语言模型（MLLMs），特别是GPT-4V的进展，提出一种新方法Make-it-Real：1）我们证明GPT-4V可有效识别并描述材质，从而构建详尽的材质库。2）通过结合视觉线索与层级文本提示，GPT-4V精确识别并将材质与3D物体的对应组件对齐。3）随后，将精准匹配的材质作为参考，根据原始漫反射贴图生成全新的SVBRDF材质，显著提升其视觉真实性。Make-it-Real实现了与3D内容创作流程的简化集成，展现出其作为3D资产开发核心工具的应用价值。