1 code implementation • 28 Mar 2024 • Anna Kukleva, Fadime Sener, Edoardo Remelli, Bugra Tekin, Eric Sauser, Bernt Schiele, Shugao Ma
Lately, there has been growing interest in adapting vision-language models (VLMs) to image and third-person video classification due to their success in zero-shot recognition.
no code implementations • 26 Mar 2024 • Sammy Christen, Shreyas Hampali, Fadime Sener, Edoardo Remelli, Tomas Hodan, Eric Sauser, Shugao Ma, Bugra Tekin
In the grasping stage, the model only generates hand motions, whereas in the interaction phase both hand and object poses are synthesized.
1 code implementation • 22 Sep 2022 • Ren Li, Benoît Guillard, Edoardo Remelli, Pascal Fua
Existing data-driven methods for draping garments over human bodies, despite being effective, cannot handle garments of arbitrary topology and are typically not end-to-end differentiable.
no code implementations • 20 Jul 2022 • Edoardo Remelli, Timur Bagautdinov, Shunsuke Saito, Tomas Simon, Chenglei Wu, Shih-En Wei, Kaiwen Guo, Zhe Cao, Fabian Prada, Jason Saragih, Yaser Sheikh
To circumvent this, we propose a novel volumetric avatar representation by extending mixtures of volumetric primitives to articulated objects.
no code implementations • 20 Jun 2021 • Benoit Guillard, Edoardo Remelli, Artem Lukoianov, Stephan R. Richter, Timur Bagautdinov, Pierre Baque, Pascal Fua
Our key insight is that by reasoning on how implicit field perturbations impact local surface geometry, one can ultimately differentiate the 3D location of surface samples with respect to the underlying deep implicit field.
no code implementations • ICCV 2021 • Benoit Guillard, Edoardo Remelli, Pierre Yvernay, Pascal Fua
Reconstructing 3D shape from 2D sketches has long been an open problem because the sketches only provide very sparse and ambiguous information.
no code implementations • 21 Dec 2020 • Mengshi Qi, Edoardo Remelli, Mathieu Salzmann, Pascal Fua
Deep learning-solutions for hand-object 3D pose and shape estimation are now very effective when an annotated dataset is available to train them to handle the scenarios and lighting conditions they will encounter at test time.
Generative Adversarial Network Unsupervised Domain Adaptation
no code implementations • NeurIPS 2020 • Benoit Guillard, Edoardo Remelli, Pascal Fua
Most state-of-the-art deep geometric learning single-view reconstruction approaches rely on encoder-decoder architectures that output either shape parametrizations or implicit representations.
1 code implementation • NeurIPS 2020 • Edoardo Remelli, Artem Lukoianov, Stephan R. Richter, Benoît Guillard, Timur Bagautdinov, Pierre Baque, Pascal Fua
Unfortunately, these methods are often not suitable for applications that require an explicit mesh-based surface representation because converting an implicit field to such a representation relies on the Marching Cubes algorithm, which cannot be differentiated with respect to the underlying implicit field.
no code implementations • CVPR 2020 • Edoardo Remelli, Shangchen Han, Sina Honari, Pascal Fua, Robert Wang
We present a lightweight solution to recover 3D pose from multi-view images captured with spatially calibrated cameras.
Ranked #4 on 3D Human Pose Estimation on Total Capture
1 code implementation • 8 Dec 2019 • Udaranga Wickramasinghe, Edoardo Remelli, Graham Knott, Pascal Fua
CNN-based volumetric methods that label individual voxels now dominate the field of biomedical segmentation.
no code implementations • 27 Jan 2019 • Edoardo Remelli, Pierre Baque, Pascal Fua
Most algorithms that rely on deep learning-based approaches to generate 3D point sets can only produce clouds containing fixed number of points.
1 code implementation • ICCV 2017 • Edoardo Remelli, Anastasia Tkach, Andrea Tagliasacchi, Mark Pauly
We present a robust algorithm for personalizing a sphere-mesh tracking model to a user from a collection of depth measurements.