Material Palette: Extraction of Materials from a Single Image, CVPR'24

Authors: Ivan Lopes (Inria), Fabio Pizzati (Oxford Uni.), Raoul de Charette (Inria) Page | arXiv | Github | Dataset (soon) Abstract: In this paper, we propose a method to extract Physically-Based-Rendering (PBR) materials from a single real-world image. We do so in two steps: first, we map regions of the image to material concepts using a diffusion model, which allows the sampling of texture images resembling each material in the scene....

December 7, 2023 · Ivan Lopes, Fabio Pizzati, and Raoul de Charette

Cross-task Attention Mechanism for Dense Multi-Task Learning, WACV'23

Authors: Ivan Lopes (Inria), Tuan-Hung Vu (Valeo.ai), Raoul de Charette (Inria) CVF | arXiv | Github Abstract: Multi-task learning has recently become a promising solution for a comprehensive understanding of complex scenes. Not only being memory-efficient, multi-task models with an appropriate design can favor exchange of complementary signals across tasks. In this work, we jointly address 2D semantic segmentation, and two geometry-related tasks, namely dense depth, surface normal estimation as well as edge estimation showing their benefit on indoor and outdoor datasets....

June 19, 2022 · Ivan Lopes, Tuan-Hung Vu, and Raoul de Charette