292 citations · 727 across the 33 of their papers we have counts for
39 papers
Anny-Fit: All-Age Human Mesh Recovery
Laura Bravo-Sánchez, Matthieu Armando, Romain Brégier +3
Recovering 3D human pose and shape from a single image remains a cornerstone of human-centric vision, yet most methods assume adult subjects and optimize each person independently.…
Human Mesh Modeling for Anny Body
Romain Brégier, Guénolé Fiche, Laura Bravo-Sánchez +5
Parametric body models provide the structural basis for many human-centric tasks, yet existing models often rely on costly 3D scans and learned shape spaces that are proprietary an…
HOSt3R: Keypoint-free Hand-Object 3D Reconstruction from RGB images
Anilkumar Swamy, Vincent Leroy, Philippe Weinzaepfel +2
Hand-object 3D reconstruction has become increasingly important for applications in human-robot interaction and immersive AR/VR experiences. A common approach for object-agnostic h…
HAMSt3R: Human-Aware Multi-view Stereo 3D Reconstruction
Sara Rojas, Matthieu Armando, Bernard Ghamen +3
Recovering the 3D geometry of a scene from a sparse set of uncalibrated images is a long-standing problem in computer vision. While recent learning-based approaches such as DUSt3R…
CondiMen: Conditional Multi-Person Mesh Recovery
Brégier Romain, Baradel Fabien, Lucas Thomas +4
Multi-person human mesh recovery (HMR) consists in detecting all individuals in a given input image, and predicting the body shape, pose, and 3D location for each detected person.…
PoseEmbroider: Towards a 3D, Visual, Semantic-aware Human Pose Representation
Ginger Delmas, Philippe Weinzaepfel, Francesc Moreno-Noguer +1
Aligning multiple modalities in a latent space, such as images and texts, has shown to produce powerful semantic visual representations, fueling tasks like image captioning, text-t…
T2LM: Long-Term 3D Human Motion Generation from Multiple Sentences
Taeryung Lee, Fabien Baradel, Thomas Lucas +2
In this paper, we address the challenging problem of long-term 3D human motion generation. Specifically, we aim to generate a long sequence of smoothly connected actions from a str…
Purposer: Putting Human Motion Generation in Context
Nicolas Ugrinovic, Thomas Lucas, Fabien Baradel +3
We present a novel method to generate human motion to populate 3D indoor scenes. It can be controlled with various combinations of conditioning signals such as a path in a scene, t…
Placing Objects in Context via Inpainting for Out-of-distribution Segmentation
Pau de Jorge, Riccardo Volpi, Puneet K. Dokania +2
When deploying a semantic segmentation model into the real world, it will inevitably encounter semantic classes that were not seen during training. To ensure a safe deployment of s…
Multi-HMR: Multi-Person Whole-Body Human Mesh Recovery in a Single Shot
Fabien Baradel, Matthieu Armando, Salma Galaaoui +4
We present Multi-HMR, a strong sigle-shot model for multi-person 3D human mesh recovery from a single RGB image. Predictions encompass the whole body, i.e., including hands and fac…
Cross-view and Cross-pose Completion for 3D Human Understanding
Matthieu Armando, Salma Galaaoui, Fabien Baradel +5
Human perception and understanding is a major domain of computer vision which, like many other vision subdomains recently, stands to gain from the use of large models pre-trained o…
SHOWMe: Benchmarking Object-agnostic Hand-Object 3D Reconstruction
Anilkumar Swamy, Vincent Leroy, Philippe Weinzaepfel +6
Recent hand-object interaction datasets show limited real object variability and rely on fitting the MANO parametric model to obtain groundtruth hand shapes. To go beyond these lim…
PoseFix: Correcting 3D Human Poses with Natural Language
Ginger Delmas, Philippe Weinzaepfel, Francesc Moreno-Noguer +1
Automatically producing instructions to modify one's posture could open the door to endless applications, such as personalized coaching and in-home physical therapy. Tackling the r…
4DHumanOutfit: a multi-subject 4D dataset of human motion sequences in varying outfits exhibiting large displacements
Matthieu Armando, Laurence Boissieux, Edmond Boyer +11
This work presents 4DHumanOutfit, a new dataset of densely sampled spatio-temporal 4D human motion data of different actors, outfits and motions. The dataset is designed to contain…
Reliability in Semantic Segmentation: Are We on the Right Track?
Pau de Jorge, Riccardo Volpi, Philip Torr +1
Motivated by the increasing popularity of transformers in computer vision, in recent times there has been a rapid development of novel architectures. While in-domain performance fo…
PoseGPT: Quantization-based 3D Human Motion Generation and Forecasting
Thomas Lucas, Fabien Baradel, Philippe Weinzaepfel +1
We address the problem of action-conditioned generation of human motion sequences. Existing work falls into two categories: forecast models conditioned on observed past motions, or…
MonoNHR: Monocular Neural Human Renderer
Hongsuk Choi, Gyeongsik Moon, Matthieu Armando +3
Existing neural human rendering methods struggle with a single image input due to the lack of information in invisible areas and the depth ambiguity of pixels in visible areas. In…
PoseScript: Linking 3D Human Poses and Natural Language
Ginger Delmas, Philippe Weinzaepfel, Thomas Lucas +2
Natural language plays a critical role in many computer vision applications, such as image captioning, visual question answering, and cross-modal retrieval, to provide fine-grained…
PoseBERT: A Generic Transformer Module for Temporal 3D Human Modeling
Fabien Baradel, Romain Brégier, Thibault Groueix +3
Training state-of-the-art models for human pose estimation in videos requires datasets with annotations that are really hard and expensive to obtain. Although transformers have bee…
Catastrophic overfitting can be induced with discriminative non-robust features
Guillermo Ortiz-Jiménez, Pau de Jorge, Amartya Sanyal +5
Adversarial training (AT) is the de facto method for building robust neural networks, but it can be computationally expensive. To mitigate this, fast single-step attacks can be use…
Make Some Noise: Reliable and Efficient Single-Step Adversarial Training
Pau de Jorge, Adel Bibi, Riccardo Volpi +4
Recently, Wong et al. showed that adversarial training with single-step FGSM leads to a characteristic failure mode named Catastrophic Overfitting (CO), in which a model becomes su…
Barely-Supervised Learning: Semi-Supervised Learning with very few labeled images
Thomas Lucas, Philippe Weinzaepfel, Gregory Rogez
This paper tackles the problem of semi-supervised learning when the set of labeled samples is limited to a small number of images per class, typically less than 10, problem that we…
Leveraging MoCap Data for Human Mesh Recovery
Fabien Baradel, Thibault Groueix, Philippe Weinzaepfel +3
Training state-of-the-art models for human body pose and shape recovery from images or videos requires datasets with corresponding annotations that are really hard and expensive to…
SMPLy Benchmarking 3D Human Pose Estimation in the Wild
Vincent Leroy, Philippe Weinzaepfel, Romain Brégier +2
Predicting 3D human pose from images has seen great recent improvements. Novel approaches that can even predict both pose and shape from a single input image have been introduced,…