1 code implementation • CVPR 2020 • Julian Chibane, Thiemo Alldieck, Gerard Pons-Moll
To solve this, we propose Implicit Feature Networks (IF-Nets), which deliver continuous outputs, can handle multiple topologies, and complete shapes for missing or sparse input data retaining the nice properties of recent learned implicit functions, but critically they can also retain detail when it is present in the input data, and can reconstruct articulated humans.
1 code implementation • NeurIPS 2020 • Julian Chibane, Aymen Mir, Gerard Pons-Moll
NDF represent surfaces at high resolutions as prior implicit models, but do not require closed surface data, and significantly broaden the class of representable shapes in the output.
1 code implementation • CVPR 2021 • Julian Chibane, Aayush Bansal, Verica Lazova, Gerard Pons-Moll
Recent neural view synthesis methods have achieved impressive quality and realism, surpassing classical pipelines which rely on multi-view reconstruction.
1 code implementation • 20 Sep 2020 • Julian Chibane, Gerard Pons-Moll
Instead, we focus on 3D texture and geometry completion from partial and incomplete 3D scans.
1 code implementation • CVPR 2023 • Ilya A. Petrov, Riccardo Marin, Julian Chibane, Gerard Pons-Moll
The intimate entanglement between objects affordances and human poses is of large interest, among others, for behavioural sciences, cognitive psychology, and Computer Vision communities.
no code implementations • 26 Oct 2020 • Alexandre Saint, Anis Kacem, Kseniya Cherenkova, Konstantinos Papadopoulos, Julian Chibane, Gerard Pons-Moll, Gleb Gusev, David Fofi, Djamila Aouada, Bjorn Ottersten
Additionally, two unique datasets of 3D scans are proposed, to provide raw ground-truth data for the benchmarks.
no code implementations • 5 May 2022 • Vladimir Guzov, Julian Chibane, Riccardo Marin, Yannan He, Yunus Saracoglu, Torsten Sattler, Gerard Pons-Moll
In order for widespread adoption of such emerging applications, the sensor setup used to capture the interactions needs to be inexpensive and easy-to-use for non-expert users.
no code implementations • 2 Jun 2022 • Julian Chibane, Francis Engelmann, Tuan Anh Tran, Gerard Pons-Moll
Indeed, we show that it is possible to train dense segmentation models using only bounding box labels.
3D Instance Segmentation 3D Semantic Instance Segmentation +2