# Make-it-Real: Unleashing Large Multimodal Model for Painting 3D Objects with Realistic Materials

arXiv: https://arxiv.org/abs/2404.16829 · HTML: https://mlsanity.com/paper/2404.16829/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Apr 2024.

## Built on by (most cited first, 17 of 17)

- [Tailor3D: Customized 3D Assets Editing and Generation with Dual-Side Images](https://mlsanity.com/paper/2407.06191/) (2024)
- [Articraft: An Agentic System for Scalable Articulated 3D Asset Generation](https://mlsanity.com/paper/2605.15187/) (2026)
- [TexGaussian: Generating High-quality PBR Material via Octree-based 3D Gaussian Splatting](https://mlsanity.com/paper/2411.19654/) (2024)
- [LiteReality: Graphics-Ready 3D Scene Reconstruction from RGB-D Scans](https://mlsanity.com/paper/2507.02861/) (2025)
- [SAMa: Material-aware 3D Selection and Segmentation](https://mlsanity.com/paper/2411.19322/) (2024)
- [VideoMat: Extracting PBR Materials from Video Diffusion Models](https://mlsanity.com/paper/2506.09665/) (2025)
- [MuMA: 3D PBR Texturing via Multi-Channel Multi-View Generation and Agentic Post-Processing](https://mlsanity.com/paper/2503.18461/) (2025)
- [MajutsuCity: Language-driven Aesthetic-adaptive City Generation with Controllable 3D Assets and Layouts](https://mlsanity.com/paper/2511.20415/) (2025)
- [Hi3DEval: Advancing 3D Generation Evaluation with Hierarchical Validity](https://mlsanity.com/paper/2508.05609/) (2025)
- [VideoMatGen: PBR Materials through Joint Generative Modeling](https://mlsanity.com/paper/2603.16566/) (2026)
- [MaRI: Material Retrieval Integration across Domains](https://mlsanity.com/paper/2503.08111/) (2025)
- [Photo3D: Advancing Photorealistic 3D Generation through Structure-Aligned Detail Enhancement](https://mlsanity.com/paper/2512.08535/) (2025)
- [PBR3DGen: A VLM-guided Mesh Generation with High-quality PBR Texture](https://mlsanity.com/paper/2503.11368/) (2025)
- [LLM-Guided Material Inference for 3D Point Clouds](https://mlsanity.com/paper/2512.03237/) (2025)
- [An evaluation of SVBRDF Prediction from Generative Image Models for Appearance Modeling of 3D Scenes](https://mlsanity.com/paper/2512.13950/) (2025)
- [GARDEN: Gravity-Aligned Reconstruction of Disentangled ENvironments from RGB images](https://mlsanity.com/paper/2606.03921/) (2026)
- [Texture Space Material Diffusion](https://mlsanity.com/paper/2609.37654/) (2026)

## Abstract (arXiv)

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.