Materialist: Physically Based Editing Using Single-Image Inverse Rendering

Understand

Achieving physically consistent image editing remains a significant challenge in computer vision.

• Existing image editing methods typically rely on neural networks, which struggle to accurately handle shadows and refractions.
• Conversely, physics-based inverse rendering often requires multi-view optimization, limiting its practicality in single-image scenarios.
• In this paper, we propose Materialist, a neural-initialized physically based rendering pipeline for single-image inverse rendering.