Transferring disentangled representations: bridging the gap between synthetic and real images
Developing meaningful and efficient representations that separate the fundamental structure of the data generation mechanism is crucial in representation learning. However, Disentangled...
HHP-Net: A light Heteroscedastic neural network for Head Pose estimation with uncertainty
In this paper we introduce a novel method to estimate the head pose of people in single images starting from a small set of head keypoints. To this purpose, we propose a regression model that...
Robots with Different Embodiments Can Express and Influence Carefulness in Object Manipulation
Humans have an extraordinary ability to communicate and read the properties of objects by simply watching them being carried by someone else. This level of communicative skills and interpretation,...
Property-Aware Robot Object Manipulation: a Generative Approach
When transporting an object, we unconsciously adapt our movement to its properties, for instance by slowing down when the item is fragile. The most relevant features of an object are immediately...
Synthesis and Execution of Communicative Robotic Movements with Generative Adversarial Networks
Object manipulation is a natural activity we perform every day. How humans handle objects can communicate not only the willfulness of the acting, or key aspects of the context where we operate,...
Action similarity judgment based on kinematic primitives
Understanding which features humans rely on -- in visually recognizing action similarity is a crucial step towards a clearer picture of human action perception from a learning and developmental...
Scale Invariant Interest Points with Shearlets
Shearlets are a relatively new directional multi-scale framework for signal analysis, which have been shown effective to enhance signal discontinuities such as edges and corners at multiple...