  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
albedo 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.
