3 citations · 4 across the 5 of their papers we have counts for
5 papers
StableMTL: Repurposing Latent Diffusion Models for Multi-Task Learning from Partially Annotated Synthetic Datasets
Anh-Quan Cao, Ivan Lopes, Raoul de Charette
Multi-task learning for dense prediction is limited by the need for extensive annotation for every task, though recent works have explored training with partial task labels. Levera…
MatSwap: Light-aware material transfers in images
Ivan Lopes, Valentin Deschaintre, Yannick Hold-Geoffroy +1
We present MatSwap, a method to transfer materials to designated surfaces in an image photorealistically. Such a task is non-trivial due to the large entanglement of material appea…
Material Transforms from Disentangled NeRF Representations
Ivan Lopes, Jean-François Lalonde, Raoul de Charette
In this paper, we first propose a novel method for transferring material transformations across different scenes. Building on disentangled Neural Radiance Field (NeRF) representati…
Material Palette: Extraction of Materials from a Single Image
Ivan Lopes, Fabio Pizzati, Raoul de Charette
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 t…
DenseMTL: Cross-task Attention Mechanism for Dense Multi-task Learning
Ivan Lopes, Tuan-Hung Vu, Raoul de Charette
Multi-task learning has recently emerged as a promising solution for a comprehensive understanding of complex scenes. In addition to being memory-efficient, multi-task models, when…