Chandran, PrashanthCiccone, LoïcZoss, GaspardBradley, DerekCeylan, DuyguLi, Tzu-Mao2025-05-092025-05-092025978-3-03868-268-41017-4656https://doi.org/10.2312/egs.20251036https://diglib.eg.org/handle/10.2312/egs20251036We 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.Attribution 4.0 International LicenseCCS Concepts: Computing methodologies → Shape modeling; AnimationComputing methodologies → Shape modelingAnimation10.2312/egs.202510364 pages