1 code implementation • 22 Feb 2024 • Fabien Baradel, Matthieu Armando, Salma Galaaoui, Romain Brégier, Philippe Weinzaepfel, Grégory Rogez, Thomas Lucas
We present Multi-HMR, a strong single-shot model for multi-person 3D human mesh recovery from a single RGB image.
Ranked #1 on 3D Human Pose Estimation on UBody
no code implementations • 15 Nov 2023 • Matthieu Armando, Salma Galaaoui, Fabien Baradel, Thomas Lucas, Vincent Leroy, Romain Brégier, Philippe Weinzaepfel, Grégory Rogez
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 on large datasets.
no code implementations • ICCV 2023 • Ginger Delmas, Philippe Weinzaepfel, Francesc Moreno-Noguer, Grégory Rogez
Automatically producing instructions to modify one's posture could open the door to endless applications, such as personalized coaching and in-home physical therapy.
1 code implementation • 21 Oct 2022 • Ginger Delmas, Philippe Weinzaepfel, Thomas Lucas, Francesc Moreno-Noguer, Grégory Rogez
Thirdly, we present a learned process for generating pose descriptions.
1 code implementation • 19 Oct 2022 • Thomas Lucas, Fabien Baradel, Philippe Weinzaepfel, Grégory Rogez
The discrete and compressed nature of the latent space allows the GPT-like model to focus on long-range signal, as it removes low-level redundancy in the input signal.
1 code implementation • 22 Aug 2022 • Fabien Baradel, Romain Brégier, Thibault Groueix, Philippe Weinzaepfel, Yannis Kalantidis, Grégory Rogez
It is simple, generic and versatile, as it can be plugged on top of any image-based model to transform it in a video-based model leveraging temporal information.
1 code implementation • 2 Feb 2022 • Pau de Jorge, Adel Bibi, Riccardo Volpi, Amartya Sanyal, Philip H. S. Torr, Grégory Rogez, Puneet K. Dokania
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 suddenly vulnerable to multi-step attacks.
1 code implementation • 18 Oct 2021 • Fabien Baradel, Thibault Groueix, Philippe Weinzaepfel, Romain Brégier, Yannis Kalantidis, Grégory Rogez
In fact, we show that simply fine-tuning the batch normalization layers of the model is enough to achieve large gains.
Ranked #59 on 3D Human Pose Estimation on 3DPW
no code implementations • 29 Sep 2021 • Pau de Jorge, Adel Bibi, Riccardo Volpi, Amartya Sanyal, Philip Torr, Grégory Rogez, Puneet K. Dokania
In this work, we methodically revisit the role of noise and clipping in single-step adversarial training.
no code implementations • CVPR 2021 • Riccardo Volpi, Diane Larlus, Grégory Rogez
In this context, we show that one way to learn models that are inherently more robust against forgetting is domain randomization - for vision tasks, randomizing the current domain's distribution with heavy image manipulations.
no code implementations • 4 Dec 2020 • Vincent Leroy, Philippe Weinzaepfel, Romain Brégier, Hadrien Combaluzier, Grégory Rogez
Predicting 3D human pose from images has seen great recent improvements.
1 code implementation • ECCV 2020 • Philippe Weinzaepfel, Romain Brégier, Hadrien Combaluzier, Vincent Leroy, Grégory Rogez
We introduce DOPE, the first method to detect and estimate whole-body 3D human poses, including bodies, hands and faces, in the wild.
no code implementations • ECCV 2020 • Anil Armagan, Guillermo Garcia-Hernando, Seungryul Baek, Shreyas Hampali, Mahdi Rad, Zhaohui Zhang, Shipeng Xie, Mingxiu Chen, Boshen Zhang, Fu Xiong, Yang Xiao, Zhiguo Cao, Junsong Yuan, Pengfei Ren, Weiting Huang, Haifeng Sun, Marek Hrúz, Jakub Kanis, Zdeněk Krňoul, Qingfu Wan, Shile Li, Linlin Yang, Dongheui Lee, Angela Yao, Weiguo Zhou, Sijia Mei, Yun-hui Liu, Adrian Spurr, Umar Iqbal, Pavlo Molchanov, Philippe Weinzaepfel, Romain Brégier, Grégory Rogez, Vincent Lepetit, Tae-Kyun Kim
To address these issues, we designed a public challenge (HANDS'19) to evaluate the abilities of current 3D hand pose estimators (HPEs) to interpolate and extrapolate the poses of a training set.
no code implementations • 16 Dec 2019 • Philippe Weinzaepfel, Grégory Rogez
Our experiments show that (a) state-of-the-art 3D convolutional neural networks obtain disappointing results on such videos, highlighting the lack of true understanding of the human actions and (b) models leveraging body language via human pose are less prone to context biases.
no code implementations • 12 Feb 2018 • Grégory Rogez, Cordelia Schmid
Here, we propose a solution to generate a large set of photorealistic synthetic images of humans with 3D pose annotations.
no code implementations • 19 Jul 2017 • Nicolas Chesneau, Grégory Rogez, Karteek Alahari, Cordelia Schmid
In this paper, we propose a new framework for action localization that tracks people in videos and extracts full-body human tubes, i. e., spatio-temporal regions localizing actions, even in the case of occlusions or truncations.
no code implementations • NeurIPS 2016 • Grégory Rogez, Cordelia Schmid
Here, we propose a solution to generate a large set of photorealistic synthetic images of humans with 3D pose annotations.
Ranked #117 on 3D Human Pose Estimation on Human3.6M (PA-MPJPE metric)