I hold two Master's degrees: one in Applied Mathematics from CentraleSupelec, where I graduated with the highest honors and achieved the best overall grade in my class, and another in Machine Learning and Computer Vision (Master MVA) from Ecole Normale Supérieure Paris-Saclay (formerly known as ENS Cachan).
In 2020, I had the opportunity to intern remotely at Adobe Research in San Jose, where I worked on primitives' fitting for large 3D point clouds.
Then, in 2022, I interned at Snap Inc. London, where my project involved 3D body mesh reconstruction from 2D images with improved 2D reprojection accuracy.
Currently, I am interning at Meta AI in London until December 2023.
We introduce MeshPose, a method to jointly tackle the 2D DensePose (DP) and 3D Human Mesh Reconstruction (HMR) problems.
We combine DensePose and HMR representations, losses, and datasets, resulting in a unified system that performs competitively on both tasks.
We introduce StyleMorph, a 3D-aware generative model that disentangles 3D shape, camera pose, object appearance, and background appearance for high quality image synthesis.
We chain 3D morphable modelling with deferred neural rendering by performing an implicit surface rendering of “Template Object Coordinates” (TOCS).
We present Cascaded Primitive Fitting Networks (CPFN) that relies on an adaptive patch sampling network to assemble detection results of global and local primitive detection networks.
As a key enabler, we present a merging formulation that dynamically aggregates the primitives across global and local scales.
We train deeper and more accurate point processing networks by introducing three modular point processing blocks that improve memory consumption and accuracy.
By combining these blocks, we design wider and deeper point-based architectures.
June to December 2023: Research Intern at Meta AI
June to December 2022: Research Intern at Snapchat AR
June to November 2020: Research Intern at Adobe Research