no code implementations • 20 Dec 2023 • Pierluigi Zama Ramirez, Luca De Luigi, Daniele Sirocchi, Adriano Cardace, Riccardo Spezialetti, Francesco Ballerini, Samuele Salti, Luigi Di Stefano
In recent years, Neural Fields (NFs) have emerged as an effective tool for encoding diverse continuous signals such as images, videos, audio, and 3D shapes.
no code implementations • 10 Feb 2023 • Luca De Luigi, Adriano Cardace, Riccardo Spezialetti, Pierluigi Zama Ramirez, Samuele Salti, Luigi Di Stefano
Implicit Neural Representations (INRs) have emerged in the last few years as a powerful tool to encode continuously a variety of different signals like images, videos, audio and 3D shapes.
no code implementations • 26 Jan 2023 • Pierluigi Zama Ramirez, Adriano Cardace, Luca De Luigi, Alessio Tonioni, Samuele Salti, Luigi Di Stefano
Besides, we propose a set of strategies to constrain the learned feature spaces, to ease learning and increase the generalization capability of the mapping network, thereby considerably improving the final performance of our framework.
no code implementations • 24 Nov 2022 • Luca De Luigi, Damiano Bolognini, Federico Domeniconi, Daniele De Gregorio, Matteo Poggi, Luigi Di Stefano
In this paper, we propose the first-ever real benchmark thought for evaluating Neural Radiance Fields (NeRFs) and, in general, Neural Rendering (NR) frameworks.
1 code implementation • CVPR 2023 • Luca De Luigi, Ren Li, Benoît Guillard, Mathieu Salzmann, Pascal Fua
Recent approaches to drape garments quickly over arbitrary human bodies leverage self-supervision to eliminate the need for large training sets.
1 code implementation • 10 Jun 2022 • Gianluca Berardi, Luca De Luigi, Samuele Salti, Luigi Di Stefano
In particular, we show that it is possible to use representation learning to learn a fixed-size, low-dimensional embedding space of trained deep models and that such space can be explored by interpolation or optimization to attain ready-to-use models.
1 code implementation • 13 Oct 2021 • Adriano Cardace, Luca De Luigi, Pierluigi Zama Ramirez, Samuele Salti, Luigi Di Stefano
We further rely on depth to generate a large and varied set of samples to Self-Train the final model.
1 code implementation • 11 Oct 2019 • Pierluigi Zama Ramirez, Claudio Paternesi, Luca De Luigi, Luigi Lella, Daniele De Gregorio, Luigi Di Stefano
Availability of a few, large-size, annotated datasets, like ImageNet, Pascal VOC and COCO, has lead deep learning to revolutionize computer vision research by achieving astonishing results in several vision tasks. We argue that new tools to facilitate generation of annotated datasets may help spreading data-driven AI throughout applications and domains.