Single-Shot Facial Appearance Acquisition without Statistical Appearance Priors

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dc.contributor.authorSoh, Guan Yuen_US
dc.contributor.authorGhosh, Abhijeeten_US
dc.contributor.editorCeylan, Duyguen_US
dc.contributor.editorLi, Tzu-Maoen_US
dc.date.accessioned2025-05-09T09:35:22Z
dc.date.available2025-05-09T09:35:22Z
dc.date.issued2025
dc.description.abstractSingle-shot in-the-wild facial reflectance acquisition has been a long-standing challenge in the field of computer graphics and computer vision. Current state-of-the-art methods are typically learning-based methods, pre-trained on a dataset of facial reflectance data. However, due to the high cost and time-consuming nature of gathering these datasets, they are usually limited in the number of subjects covered and hence are prone to biases in the dataset. To this end, we propose a novel multi-stage guided optimization with differentiable rendering to tackle this problem, without the use of statistical facial appearance priors. This makes our method immune to these biases, and we demonstrate the advantage with qualitative and quantitative evaluations against current state-of-the-art methods.en_US
dc.description.sectionheadersShort Paper 2
dc.description.seriesinformationEurographics 2025 - Short Papers
dc.identifier.doi10.2312/egs.20251035
dc.identifier.isbn978-3-03868-268-4
dc.identifier.issn1017-4656
dc.identifier.pages4 pages
dc.identifier.urihttps://doi.org/10.2312/egs.20251035
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/egs20251035
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Computing methodologies→Reflectance modeling
dc.subjectComputing methodologies→Reflectance modeling
dc.titleSingle-Shot Facial Appearance Acquisition without Statistical Appearance Priorsen_US
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