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I am a research scientist at Naver Labs Europe in Grenoble, France, working on human-centric 3D vision.
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A simple yet effective model for multi-person whole-bdy 3d human pose estimation running in real-time on a GPU and reaching SotA results. |
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A self-supervised pre-training strategy for human-centric 3D vision. |
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A new high-quality textured meshes dataset of hand holding an object. |
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PoseGPT generates a human motion, conditioned on an action label, a duration and optionally on an observed pas human motion. We learn to quantize the human motion into a discrete latent space and we train a GPT-like model to sequentially predicts next discrete latent indices. |
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We propose a generic transformer model for temporal modeling of human and hand shape. We apply this model to different tasks such as pose estimation and future pose prediction. PoseBERT is able to denoise and interpolate which is very important for deploying pose estimation on he wild. |
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We propose a model learned in a unsupervised manner which is able to perform counterfactual predictions in pixel space. |
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We show that Mocap data can be used for improving image-based and video-based human mesh recovery methods. We propose a video-based transformer model called PoseBERT which is trained on synthetic data only. |
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We introduce a new problem of counterfactual learning of object mechanics from visual input and a benchmark called CoPhy. |
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Self-supervised video representation by leveraging ASR and long videos via noise contrastive estimation. |
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A model capable of learning to reason about semantically meaningful spatio-temporal interactions in videos. |
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Human activity recogntion using skeleton data and RGB. We propose a network able to focus on relevant parts of the RGB stream given deep features extracted from the pose stream. |
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We propose a new method for human action recognition relying on RGB data only. A visual attention module is able to extract glimpses within each frame. Resulting local descriptors are soft-assigned to distributed workers which are finally classifying the video. |
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A new spatio-temporal attention based mechanism for human action recognition able to automatically attend to most important human hands and detect the most discriminative moments in an action. |
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We introduce a new dataset for Domain Adaptation and show a comparaison between shallow and deep methods based on Maximum Mean Discrepancy. |
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We introduce an attention-based mechanism around hands on RGB videos conditioned on features extracted from human 3D pose. |
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We introduce a new method based on Conditional Maximum Mean Discrepancy for domain adaptation on image classification. |
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