41 citations · 49 across the 28 of their papers we have counts for
6 papers · 1 filter
Reliability in Semantic Segmentation: Can We Use Synthetic Data?
Thibaut Loiseau, Tuan-Hung Vu, Mickael Chen +2
Assessing the robustness of perception models to covariate shifts and their ability to detect out-of-distribution (OOD) inputs is crucial for safety-critical applications such as a…
ManiPose: Manifold-Constrained Multi-Hypothesis 3D Human Pose Estimation
Cédric Rommel, Victor Letzelter, Nermin Samet +4
We propose ManiPose, a manifold-constrained multi-hypothesis model for human-pose 2D-to-3D lifting. We provide theoretical and empirical evidence that, due to the depth ambiguity i…
PointBeV: A Sparse Approach to BeV Predictions
Loick Chambon, Eloi Zablocki, Mickael Chen +3
Bird's-eye View (BeV) representations have emerged as the de-facto shared space in driving applications, offering a unified space for sensor data fusion and supporting various down…
ToddlerDiffusion: Interactive Structured Image Generation with Cascaded Schrödinger Bridge
Eslam Abdelrahman, Liangbing Zhao, Vincent Tao Hu +3
Diffusion models break down the challenging task of generating data from high-dimensional distributions into a series of easier denoising steps. Inspired by this paradigm, we propo…
Gradpaint: Gradient-Guided Inpainting with Diffusion Models
Asya Grechka, Guillaume Couairon, Matthieu Cord
Denoising Diffusion Probabilistic Models (DDPMs) have recently achieved remarkable results in conditional and unconditional image generation. The pre-trained models can be adapted…
DiffHPE: Robust, Coherent 3D Human Pose Lifting with Diffusion
Cédric Rommel, Eduardo Valle, Mickaël Chen +4
We present an innovative approach to 3D Human Pose Estimation (3D-HPE) by integrating cutting-edge diffusion models, which have revolutionized diverse fields, but are relatively un…