Boosting 3D Object Generation through PBR Materials

TL;DR

This paper introduces a novel approach that enhances 3D object generation quality by integrating PBR material components, leading to more realistic rendering, relighting, and geometry alignment, applicable across various existing methods.

Contribution

It proposes a PBR-based framework that considers albedo, roughness, metalness, and bump maps, using fine-tuned diffusion models for consistent material extraction and semi-automatic adjustments for practicality.

Findings

Significantly improves realism and quality of generated 3D objects.

Enables natural relighting effects and better geometry alignment.

Beneficial across multiple state-of-the-art generation methods.

Abstract

Automatic 3D content creation has gained increasing attention recently, due to its potential in various applications such as video games, film industry, and AR/VR. Recent advancements in diffusion models and multimodal models have notably improved the quality and efficiency of 3D object generation given a single RGB image. However, 3D objects generated even by state-of-the-art methods are still unsatisfactory compared to human-created assets. Considering only textures instead of materials makes these methods encounter challenges in photo-realistic rendering, relighting, and flexible appearance editing. And they also suffer from severe misalignment between geometry and high-frequency texture details. In this work, we propose a novel approach to boost the quality of generated 3D objects from the perspective of Physics-Based Rendering (PBR) materials. By analyzing the components of PBR materials, we choose to consider albedo, roughness, metalness, and bump maps. For albedo and bump maps, we leverage Stable Diffusion fine-tuned on synthetic data to extract these values, with novel usages of these fine-tuned models to obtain 3D consistent albedo UV and bump UV for generated objects. In terms of roughness and metalness maps, we adopt a semi-automatic process to provide room for interactive adjustment, which we believe is more practical. Extensive experiments demonstrate that our model is generally beneficial for various state-of-the-art generation methods, significantly boosting the quality and realism of their generated 3D objects, with natural relighting effects and substantially improved geometry.