I am a Research Scientist at Meta working on Computer Vision and Generative AI. I did my PhD in Computer Science in the Imagine lab at ENPC under the guidance of Mathieu Aubry. During my PhD, I was fortunate to work with Jean Ponce (Inria), Matthew Fisher (Adobe Research), Alyosha Efros and Angjoo Kanazawa (UC Berkeley). Before that, I completed my engineer's degree (=M.Sc.) at Mines Paris.
My research focuses on building machines that learn to perceive things from images without any annotations, through self-supervised and unsupervised learning techniques. Representative papers are highlighted.
We compute a primitive-based 3D reconstruction from multiple views by optimizing textured superquadric meshes with learnable transparency.
We introduce MACARONS, a method that learns in a self-supervised fashion to explore new environments and reconstruct them in 3D using RGB images only.
We build upon sprite-based image decomposition approaches to design a generative method for character analysis and recognition in text lines.
A Transformer-based framework to evaluate off-the-shelf features (object-centric and dense representations) for the reasoning task of VQA.
We present UNICORN, a self-supervised approach leveraging the consistency across different single-view images for high-quality 3D reconstructions.
We characterize 3D shapes as affine transformations of linear families learned without supervision, and showcase its advantages on large shape collections.
We discover the objects recurrent in unlabeled image collections by modeling images as a composition of learnable sprites.
A simple adaptation of K-means to make it work on pixels! We align prototypes to each sample image before computing cluster distances.