I am a Research Intern at Disney Research|Studios and hold a PhD from INRIA in Paris, directed by Raoul de Charette. My doctoral work involved extensive collaboration, allowing me to draw my own path between computer vision and graphics. My research focuses on material estimation from images and controllable image generation. My academic training began in physics and engineering sciences, and I transitioned to computer science during engineering school. I received an MSc degree from Télécom SudParis after a master's program at Télécom Paris, and completed an exchange semester abroad at Linköping University.
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MatSwap enables photorealistic transfer of a given material onto a target surface in an image, handling complex interactions of appearance, geometry, and lighting without requiring manual input or UV mapping.
A method to transfer material transformations across scenes. Building on disentangled NeRF representations, our approach learns to map Bidirectional Reflectance Distribution Functions (BRDF) from pairs of scenes observed in varying conditions.
Material Palette extracts a palette of PBR materials (albedo, normals, and roughness) from a single real-world image using diffusion priors.
This work proposes an architecture to perform multi-task learning by combining information from different tasks using correlation-guided and self-attention mechanisms.
End of course computer graphics project, coded in C and GLSL.
Proof of concept project for a node-based editor to create systems with a visual interface.