Publications

Recently, learning frameworks have shown the capability of inferring the accurate shape, pose, and texture of an object from a single RGB image. However, current methods are trained on image collections of a single category in order to exploit specific priors, and they often make use of category-specific 3D templates. In this paper, we present an alternative approach that infers the textured mesh of objects combining a series of deformable 3D models and a set of instance-specific deformation, pose, and texture. Differently from previous works, our method is trained with images of multiple object categories using only foreground masks and rough camera poses as supervision. Without specific 3D templates, the framework learns category-level models which are deformed to recover the 3D shape of the depicted object. The instance-specific deformations are predicted independently for each vertex of the learned 3D mesh, enabling the dynamic subdivision of the mesh during the training process. Experiments show that the proposed framework can distinguish between different object categories and learn category-specific shape priors in an unsupervised manner. Predicted shapes are smooth and can leverage from multiple steps of subdivision during the training process, obtaining comparable or state-of-the-art results on two public datasets. Models and code are publicly released.

Embodied AI has been recently gaining attention as it aims to foster the development of autonomous and intelligent agents. In this paper, we devise a novel embodied setting in which an agent needs to explore a previously unknown environment while recounting what it sees during the path. In this context, the agent needs to navigate the environment driven by an exploration goal, select proper moments for description, and output natural language descriptions of relevant objects and scenes. Our model integrates a novel self-supervised exploration module with penalty, and a fully-attentive captioning model for explanation. Also, we investigate different policies for selecting proper moments for explanation, driven by information coming from both the environment and the navigation. Experiments are conducted on photorealistic environments from the Matterport3D dataset and investigate the navigation and explanation capabilities of the agent as well as the role of their interactions.

Morphing attack is an important security threat for automatic face recognition systems. High-quality morphed images, i.e. images without significant visual artifacts such as ghosts, noise, and blurring, exhibit higher chances of success, being able to fool both human examiners and commercial face verification algorithms. Therefore, the availability of large sets of high-quality morphs is fundamental for training and testing robust morphing attack detection algorithms. However, producing a high-quality morphed image is an expensive and time-consuming task since manual post-processing is generally required to remove the typical artifacts generated by landmark-based morphing techniques. This work describes an approach based on the Conditional Generative Adversarial Network paradigm for automated morphing artifact retouching and the use of Attention Maps to guide the generation process and limit the retouch to specific areas. In order to work with high-resolution images, the framework is applied on different facial crops, which, once processed and retouched, are accurately blended to reconstruct the whole morphed face. Specifically, we focus on four different squared face regions, i.e. the right and left eyes, the nose, and the mouth, that are frequently affected by artifacts. Several qualitative and quantitative experimental evaluations have been conducted to confirm the effectiveness of the proposal in terms of, among the others, pixel-wise metrics, identity preservation, and human observer analysis. Results confirm the feasibility and the accuracy of the proposed framework.

Human Pose Estimation is a fundamental task for many applications in the Computer Vision community and it has been widely investigated in the 2D domain, i.e. intensity images. Therefore, most of the available methods for this task are mainly based on 2D Convolutional Neural Networks and huge manually-annotated RGB datasets, achieving stunning results. In this paper, we propose RefiNet, a multi-stage framework that regresses an extremely-precise 3D human pose estimation from a given 2D pose and a depth map. The framework consists of three different modules, each one specialized in a particular refinement and data representation, i.e. depth patches, 3D skeleton and point clouds. Moreover, we present a new dataset, called Baracca, acquired with RGB, depth and thermal cameras and specifically created for the automotive context. Experimental results confirm the quality of the refinement procedure that largely improves the human pose estimations of off-the-shelf 2D methods.

Given the overwhelming impact of machine learning on the last decade, several libraries and frameworks have been developed in recent years to simplify the design and training of neural networks, providing array-based programming, automatic differentiation and user-friendly access to hardware accelerators. None of those tools, however, was designed with native and transparent support for Cloud Computing or heterogeneous High-Performance Computing (HPC). The DeepHealth Toolkit is an open source Deep Learning toolkit aimed at boosting productivity of data scientists operating in the medical field by providing a unified framework for the distributed training of neural networks, which is able to leverage hybrid HPC and cloud environments in a transparent way for the user. The toolkit is composed of a Computer Vision library, a Deep Learning library, and a front-end for non-expert users; all of the components are focused on the medical domain, but they are general purpose and can be applied to any other field. In this paper, the principles driving the design of the DeepHealth libraries are described, along with details about the implementation and the interaction between the different elements composing the toolkit. Finally, experiments on common benchmarks prove the efficiency of each separate component and of the DeepHealth Toolkit overall.

Event cameras are biologically-inspired sensors that gather the temporal evolution of the scene. They capture pixel-wise brightness variations and output a corresponding stream of asynchronous events. Despite having multiple advantages with respect to conventional cameras, their use is limited due to the scarce compatibility of asynchronous event streams with traditional data processing and vision algorithms. In this regard, we present a framework that synthesizes RGB frames from the output stream of an event camera and an initial or a periodic set of color key-frames. The deep learning-based frame synthesis framework consists of an adversarial image-to-image architecture and a recurrent module. Two public event-based datasets, DDD17 and MVSEC, are used to obtain qualitative and quantitative per-pixel and perceptual results. In addition, we converted into event frames two additional wellknown datasets, namely Kitti and Cityscapes, in order to present semantic results, in terms of object detection and semantic segmentation accuracy. Extensive experimental evaluation confirm the quality and the capability of the proposed approach of synthesizing frame sequences from color key-frames and sequences of intermediate events.

The slaughterhouse can act as a valid checkpoint to estimate the prevalence and the economic impact of diseases in farm animals. At present, scoring lesions is a challenging and time‐consuming activity, which is carried out by veterinarians serving the slaughter chain. Over recent years, artificial intelligence(AI) has gained traction in many fields of research, including livestock production. In particular, AI‐based methods appear able to solve highly repetitive tasks and to consistently analyze large amounts of data, such as those collected by veterinarians during postmortem inspection in high‐throughput slaughterhouses. The present study aims to develop an AI‐based method capable of recognizing and quantifying enzootic pneumonia‐like lesions on digital images captured from slaughtered pigs under routine abattoir conditions. Overall, the data indicate that the AI‐based method proposed herein could properly identify and score enzootic pneumonia‐like lesions without interfering with the slaughter chain routine. According to European legislation, the application of such a method avoids the handling of carcasses and organs, decreasing the risk of microbial contamination, and could provide further alternatives in the field of food hygiene.