no code implementations • 16 Apr 2024 • Giuseppe Tarollo, Tomaso Fontanini, Claudio Ferrari, Guido Borghi, Andrea Prati
Among all the explored techniques, Semantic Image Synthesis (SIS) methods, whose goal is to generate an image conditioned on a semantic segmentation mask, are the most promising, even though preserving the perceived identity of the input subject is not their main concern.
no code implementations • 19 Mar 2024 • Federico Nocentini, Claudio Ferrari, Stefano Berretti
The domain of 3D talking head generation has witnessed significant progress in recent years.
1 code implementation • 19 Mar 2024 • Alex Ergasti, Claudio Ferrari, Tomaso Fontanini, Massimo Bertozzi, Andrea Prati
To address that, in this paper we propose a SIS framework based on a novel Latent Diffusion Model architecture for human face generation and editing that is both able to reproduce and manipulate a real reference image and generate diversity-driven results.
no code implementations • 16 Mar 2024 • Federico Nocentini, Thomas Besnier, Claudio Ferrari, Sylvain Arguillere, Stefano Berretti, Mohamed Daoudi
Speech-driven 3D talking heads generation has emerged as a significant area of interest among researchers, presenting numerous challenges.
2 code implementations • 30 Aug 2023 • Tomaso Fontanini, Claudio Ferrari, Giuseppe Lisanti, Massimo Bertozzi, Andrea Prati
Thus, they tend to overlook global image statistics, ultimately leading to unconvincing local style editing and causing global inconsistencies such as color or illumination distribution shifts.
1 code implementation • 11 Jul 2023 • Tomaso Fontanini, Claudio Ferrari, Massimo Bertozzi, Andrea Prati
Also, we show our model can be put before a SIS generator, opening the way to a fully automatic generation control of both shape and texture.
1 code implementation • 2 Jun 2023 • Federico Nocentini, Claudio Ferrari, Stefano Berretti
This paper presents a novel approach for generating 3D talking heads from raw audio inputs.
no code implementations • 29 Jul 2022 • Naima Otberdout, Claudio Ferrari, Mohamed Daoudi, Stefano Berretti, Alberto del Bimbo
We thus propose a new model that generates transitions between different expressions, and synthesizes long and composed 4D expressions.
1 code implementation • 23 Jun 2022 • Claudio Ferrari, Matteo Serpentoni, Stefano Berretti, Alberto del Bimbo
Deep learning advanced face recognition to an unprecedented accuracy.
1 code implementation • ICLR 2022 • Claudio Ferrari, Mark Niklas Muller, Nikola Jovanovic, Martin Vechev
State-of-the-art neural network verifiers are fundamentally based on one of two paradigms: either encoding the whole verification problem via tight multi-neuron convex relaxations or applying a Branch-and-Bound (BaB) procedure leveraging imprecise but fast bounding methods on a large number of easier subproblems.
1 code implementation • 10 Feb 2022 • Romeo Valentin, Claudio Ferrari, Jérémy Scheurer, Andisheh Amrollahi, Chris Wendler, Max B. Paulus
We pose this task as a supervised learning problem: First, we compile a large dataset of the solver performance for various configurations and all provided MILP instances.
no code implementations • CVPR 2022 • Naima Otberdout, Claudio Ferrari, Mohamed Daoudi, Stefano Berretti, Alberto del Bimbo
This allows us to learn how the motion of a sparse set of landmarks influences the deformation of the overall face surface, independently from the identity.
no code implementations • 10 Sep 2020 • Jérémy Scheurer, Claudio Ferrari, Luis Berenguer Todo Bom, Michaela Beer, Werner Kempf, Luis Haug
Second, using the segmentation map and the original image, we are able to predict if a patient has MF or Eczema.
no code implementations • 27 Aug 2020 • Claudio Ferrari, Lorenzo Berlincioni, Marco Bertini, Alberto del Bimbo
As additional contribution, we enrich the original dataset by using the annotated landmarks to deform and project the 3DMM onto the images.
1 code implementation • 6 Jun 2020 • Claudio Ferrari, Stefano Berretti, Pietro Pala, Alberto del Bimbo
The 3D Morphable Model (3DMM) is a powerful statistical tool for representing 3D face shapes.
no code implementations • 11 Feb 2019 • Claudio Ferrari, Stefano Berretti, Alberto del Bimbo
In this report, we provide additional and corrected results for the paper "Extended YouTube Faces: a Dataset for Heterogeneous Open-Set Face Identification".
no code implementations • NeurIPS 2018 • Hoda Heidari, Claudio Ferrari, Krishna P. Gummadi, Andreas Krause
We draw attention to an important, yet largely overlooked aspect of evaluating fairness for automated decision making systems---namely risk and welfare considerations.