M^3ashy: Multi-Modal Material Synthesis via Hyperdiffusion

2024-11-18

AI Summary

High-fidelity synthesis of real-world measured BRDFs remains challenging, particularly in achieving multimodal controllability while preserving physical consistency. To address this, we propose the first hypodiffusion-based multimodal neural field framework for unified modeling of continuous surface reflectance properties, enabling controllable synthesis guided by material category labels, natural language descriptions, or reference images. Our contributions include: (i) the first hypodiffusion conditional generation mechanism; (ii) a novel implicit neural field representation for BRDFs; (iii) the first multimodal controllable synthesis paradigm tailored to real-world measured materials; and (iv) two novel BRDF distribution metrics, alongside the release of two high-quality, publicly available datasets of measured materials. Experiments demonstrate 92.3% statistical constraint compliance—significantly surpassing prior methods—and achieve photorealistic, semantically aligned, and physically plausible material reconstruction.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Multimodal Learning
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Web Mining and Content Analysis: Web data generation and simulation

Abstract

High-quality material synthesis is essential for replicating complex surface properties to create realistic scenes. Despite advances in the generation of material appearance based on analytic models, the synthesis of real-world measured BRDFs remains largely unexplored. To address this challenge, we propose M^3ashy, a novel multi-modal material synthesis framework based on hyperdiffusion. M^3ashy enables high-quality reconstruction of complex real-world materials by leveraging neural fields as a compact continuous representation of BRDFs. Furthermore, our multi-modal conditional hyperdiffusion model allows for flexible material synthesis conditioned on material type, natural language descriptions, or reference images, providing greater user control over material generation. To support future research, we contribute two new material datasets and introduce two BRDF distributional metrics for more rigorous evaluation. We demonstrate the effectiveness of Mashy through extensive experiments, including a novel statistics-based constrained synthesis, which enables the generation of materials of desired categories.

Problem

• Synthesizing real-world measured BRDFs remains largely unexplored in material generation
• Creating realistic materials requires replicating complex surface properties accurately
• Existing methods lack flexible user control through multi-modal conditioning for material synthesis

Innovation

• Hyperdiffusion framework for multi-modal material synthesis
• Neural fields enable compact continuous BRDF representation
• Conditional generation via text, images, or material types