Bertrand Luvison — accepted AI-conference papers

2026

Improving Controllable Generation: Faster Training and Better Performance via x0-Supervision

Amadou S. Sangare, Adrien Maglo, Mohamed Chaouch, Bertrand Luvison

CVPR 2026

Text-to-Image (T2I) diffusion/flow models have recently achieved remarkable progress in visual fidelity and text alignment. However, they remain limited when users need to precisely control image layouts, something that natural language alone cannot reliably express. Controllable generation methods

Cited by 0 Source ↗code ↗Scholar ↗

2020

PandaNet: Anchor-Based Single-Shot Multi-Person 3D Pose Estimation

Abdallah Benzine, Florian Chabot, Bertrand Luvison, Quoc Cuong Pham, Catherine Achard

CVPR 2020poster

Recently, several deep learning models have been proposed for 3D human pose estimation. Nevertheless, most of these approaches only focus on the single-person case or estimate 3D pose of a few people at high resolution. Furthermore, many applications such as autonomous driving or crowd analysis requ…

Cited by 85 PDF ↗Scholar ↗