Guénolé Fiche

Since October 2021, I am a PhD candidate in the AIMAC team at CentraleSupélec, under the supervision of Renaud Séguier and Simon Leglaive. In 2023, I visited IRI with Francesc Moreno-Noguer and Antonio Agudo. My research focuses on Computer Vision and Deep Learning. In particular, I work on 3D human pose and shape estimation from images and videos, with a focus on weakly-supervised methods (generative models, self-supervised learning).

Prior to that, I was a student at INSA Rouen Normandie, where I graduated in Mathematical and software engineering.

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This work proposes a new approach to human mesh recovery (HMR) based on masked generative modeling. By tokenizing the human pose and shape, we formulate the HMR task as generating a sequence of discrete tokens conditioned on an input image.

This work introduces a novel paradigm to address the human pose and shape estimation problem, involving a low-dimensional discrete latent representation of the human mesh and framing human pose and shape estimation as a classification task.

We introduce a motion prior to capture the short-term dependencies of human motion and an unsupervised learned denoising method unifying regression- and optimization-based approaches in a single framework for real-time 3D human pose estimation.

We introduce SwimXYZ, a synthetic dataset of swimming motions and videos. SwimXYZ contains 3.4 million frames annotated with ground truth 2D and 3D joints, as well as 240 sequences of swimming motions in the SMPL parameters format.

Website source code borrowed from Jon Barron's public academic website.