1 code implementation • 15 Sep 2021 • Angel Martínez-González, Michael Villamizar, Jean-Marc Odobez
We propose to leverage Transformer architectures for non-autoregressive human motion prediction.
1 code implementation • 10 Nov 2020 • Angel Martínez-González, Michael Villamizar, Olivier Canévet, Jean-Marc Odobez
We propose to leverage recent advances in reliable 2D pose estimation with Convolutional Neural Networks (CNN) to estimate the 3D pose of people from depth images in multi-person Human-Robot Interaction (HRI) scenarios.
no code implementations • 2 Dec 2019 • Angel Martínez-González, Michael Villamizar, Olivier Canévet, Jean-Marc Odobez
i) we study several CNN architecture designs combining pose machines relying on the cascade of detectors concept with lightweight and efficient CNN structures; ii) to address the need for large training datasets with high variability, we rely on semi-synthetic data combining multi-person synthetic depth data with real sensor backgrounds; iii) we explore domain adaptation techniques to address the performance gap introduced by testing on real depth images; iv) to increase the accuracy of our fast lightweight CNN models, we investigate knowledge distillation at several architecture levels which effectively enhance performance.
no code implementations • 30 Oct 2019 • Angel Martínez-González, Michael Villamizar, Olivier Canévet, Jean-Marc Odobez
(i) we propose a fast and efficient network based on residual blocks (called RPM) for body landmark localization from depth images; (ii) we created a public dataset DIH comprising more than 170k synthetic images of human bodies with various shapes and viewpoints as well as real (annotated) data for evaluation; (iii) we show that our model trained on synthetic data from scratch can perform well on real data, obtaining similar results to larger models initialized with pre-trained networks.