Materialist: Neural-Initialized Physically Based Rendering for Single-Image Editing
A team of researchers has introduced Materialist, a novel pipeline for single-image inverse rendering that employs a neural-initialized physically based approach. This technique leverages neural networks to forecast initial material characteristics, which are subsequently refined through progressive differentiable rendering. As a result, it facilitates material modifications, object additions, relighting, and the adjustment of material transparency through ray-traced refraction. In contrast to current methods that either face challenges with shadows and refractions or depend on physics-based strategies needing multi-view optimization, Materialist merges neural initialization with physics-based refinement specifically for single-image contexts. The full paper can be accessed on arXiv.
Key facts
- Materialist is a neural-initialized physically based rendering pipeline for single-image inverse rendering.
- It uses neural networks to predict initial material properties.
- Properties are optimized via progressive differentiable rendering.
- Applications include material editing, object insertion, relighting, and transparency editing.
- Existing neural methods struggle with shadows and refractions.
- Physics-based methods often require multi-view optimization.
- The paper is on arXiv with ID 2501.03717.
- It was announced as a replace-cross type.
Entities
Institutions
- arXiv