Neural Facial Deformation Transfer

Abstract

We address the practical problem of generating facial blendshapes and reference animations for a new 3D character in production environments where blendshape expressions and reference animations are readily available on a pre-defined template character. We propose Neural Facial Deformation Transfer (NFDT); a data-driven approach to transfer facial expressions from such a template character to new target characters given only the target’s neutral shape. To accomplish this, we first present a simple data generation strategy to automatically create a large training dataset consisting of pairs of template and target character shapes in the same expression. We then leverage this dataset through a decoder-only transformer that transfers facial expressions from the template character to a target character in high fidelity. Through quantitative evaluations and a user study, we demonstrate that NFDT surpasses the previous state-of-the-art in facial expression transfer. NFDT provides good results across varying mesh topologies, generalizes to humanoid creatures, and can save time and cost in facial animation workflows.

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Bibtex:

@inproceedings{10.2312:egs.20251036,
booktitle = {Eurographics 2025 - Short Papers},
editor = {Ceylan, Duygu and Li, Tzu-Mao},
title = ,
author = {Chandran, Prashanth and Ciccone, Loïc and Zoss, Gaspard and Bradley, Derek},
year = {2025},
publisher = {The Eurographics Association},
ISSN = {1017-4656},
ISBN = {978-3-03868-268-4},
DOI = {10.2312/egs.20251036}
}