3D Computer Vision for Animals

Thursday 15th December 2022 14:15/14:30 – 16:00 CET

 

Dr. Silvia Zuffi online

Abstract

Animals are an important resource for our society, but unfortunately they are often threatened and over exploited by humans. Computer vision can greatly contribute to animal conservation and wellbeing by providing non-invasive tools for capturing animal behaviour. Animals communicate mostly with body posture and sound, their health conditions are often related to shape changes. In this talk I will present my work on taking a 3D perspective when looking at animals, specifically through generative models of animal shape for 3D pose and shape reconstruction from monocular data.

Short Bio

Dr. Silvia Zuffi is currently a research scientist at IMATI-CNR. She graduated in Electronic Engineering at the University of Bologna in 1995. After graduation, she worked for some time in the industry, where she contributed to realize a system for the forecast of the tide in Venice. Before starting her PhD in 2009, she worked as research scientist at ITC-CNR (Milan, Italy), where she worked on color imaging, specifically multispectral imaging and reproduction, and readability of colored text on Web pages. In 2015, she received her PhD in Computer Science at Brown University, under the supervision of Prof. Michael J. Black. Her research interest is animal and human pose estimation from images and videos, and her main contribution to the field is the exploration of realistic 2D and 3D models of human body shape and 3D models of animals for estimating pose and articulated motion in unconstrained images, videos, and from 3D data.

Link
PRESENTATION

More events

Cookie Settings

A AIDA - AI Doctoral Academy may use cookies to remember your login data, collect statistics to optimize the functionality of the site and to perform marketing actions based on your interests.


These cookies are necessary to allow the main functionality of the website and are automatically activated when you use this website.
These cookies allow us to analyze the use of the website, so that we can measure and improve its performance.
Allow you to stay in touch with your social network, share content, send and post comments.

Required Cookies They allow you to personalize the commercial offers that are presented to you, directing them to your interests. They can be own or third party cookies. We warn you that, even if you do not accept these cookies, you will receive commercial offers, but without meeting your preferences.

Functional Cookies They offer a more personalized and complete experience, allow you to save preferences, show you content relevant to your taste and send you the alerts you have requested.

Advertising Cookies Allow you to stay in touch with your social network, share content, send and post comments.