Implicit Shape Avatar Generalization across Pose and Identity
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Date
2025
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
The creation of realistic animated avatars has become a hot-topic in both academia and the creative industry. Recent advancements in deep learning and implicit representations have opened new research avenues, particularly in enhancing avatar details with lightweight models. This paper introduces an improvement over the state-of-the-art implicit Fast-SNARF method to permit generalization to novel motions and shape identities. Fast-SNARF trains two networks: an occupancy network to predict the shape of a character in canonical space, and a Linear Blend Skinning network to deform it into arbitrary poses. However, it requires a separated model for each subject. We extend this work by conditioning both networks on an identity parameter, enabling a single model to generalize across multiple identities, without increasing the model's size, compared to Fast-SNARF.
Description
CCS Concepts: Computing methodologies → Motion processing; Mesh models
@inproceedings{10.2312:egs.20251049,
booktitle = {Eurographics 2025 - Short Papers},
editor = {Ceylan, Duygu and Li, Tzu-Mao},
title = {{Implicit Shape Avatar Generalization across Pose and Identity}},
author = {Loranchet, Guillaume and Hellier, Pierre and Schnitzler, Francois and Boukhayma, Adnane and Regateiro, Joao and Multon, Franck},
year = {2025},
publisher = {The Eurographics Association},
ISSN = {1017-4656},
ISBN = {978-3-03868-268-4},
DOI = {10.2312/egs.20251049}
}