AvatarizeMe: A Fast Software Tool for Transforming Selfies into Animatable Lifelike Avatars Using Machine Learning

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Date
2023
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
Creating realistic avatars that faithfully replicate facial features from single-input images is a challenging task in computer graphics, virtual communication, and interactive entertainment. These avatars have the potential to revolutionize virtual experiences by enhancing user engagement and personalization. However, existing methods, such as 3D facial capture systems, are costly and complex. Our approach adopts the 3D Morphable Face Model (3DMM) method to create avatars with remarkably realistic features in a bunch of seconds, using only a single input image. Our method extends beyond facial shape resemblance; it meticulously generates both facial and bodily textures, enhancing overall likeness. Within Unreal Engine 5, our avatars come to life with real-time body and facial animations. This is made possible through a versatile skeleton for body and head movements and a suite of 52 face blendshapes, enabling the avatar to convey emotions and expressions with fidelity. This poster presents our approach, bridging the gap between reality and virtual representation, and opening doors to immersive virtual experiences with lifelike avatars.
Description

CCS Concepts: Computing methodologies -> Computer vision; Machine learning; Parametric curve and surface models; Texturing

        
@inproceedings{
10.2312:stag.20231304
, booktitle = {
Smart Tools and Applications in Graphics - Eurographics Italian Chapter Conference
}, editor = {
Banterle, Francesco
 and
Caggianese, Giuseppe
 and
Capece, Nicola
 and
Erra, Ugo
 and
Lupinetti, Katia
 and
Manfredi, Gilda
}, title = {{
AvatarizeMe: A Fast Software Tool for Transforming Selfies into Animatable Lifelike Avatars Using Machine Learning
}}, author = {
Manfredi, Gilda
 and
Capece, Nicola
 and
Erra, Ugo
}, year = {
2023
}, publisher = {
The Eurographics Association
}, ISSN = {
2617-4855
}, ISBN = {
978-3-03868-235-6
}, DOI = {
10.2312/stag.20231304
} }
Citation