Please use this identifier to cite or link to this item:
|Title:||Conditional adversarial synthesis of 3D facial action unit||Authors:||Liu, Zhilei
|Keywords:||Engineering::Computer science and engineering||Issue Date:||2019||Source:||Liu, Z., Song, G., Cai, J., Cham, T.-J., & Zhang, J. (2019). Conditional adversarial synthesis of 3D facial action unit. Neurocomputing, 355, 200-208. doi:10.1016/j.neucom.2019.05.003||Journal:||Neurocomputing||Abstract:||Employing deep learning-based approaches for fine-grained facial expression analysis, such as those involving the estimation of Action Unit (AU) intensities, is difficult due to the lack of a large-scale dataset of real faces with sufficiently diverse AU labels for training. In this paper, we consider how AU-level facial image synthesis can be used to substantially augment such a dataset. We propose an AU synthesis framework that combines the well-known 3D Morphable Model (3DMM), which intrinsically disentangles expression parameters from other face attributes, with models that adversarially generate 3DMM expression parameters conditioned on given target AU labels, in contrast to the more conventional approach of generating facial images directly. In this way, we are able to synthesize new combinations of expression parameters and facial images from desired AU labels. Extensive quantitative and qualitative results on the benchmark DISFA dataset and BP4D dataset demonstrate the effectiveness of our method on 3DMM facial expression parameter synthesis and data augmentation for deep learning-based AU intensity estimation.||URI:||https://hdl.handle.net/10356/138268||ISSN:||0925-2312||DOI:||10.1016/j.neucom.2019.05.003||Rights:||© 2019 Elsevier B.V. All rights reserved.||Fulltext Permission:||none||Fulltext Availability:||No Fulltext|
|Appears in Collections:||IMI Journal Articles|
Updated on Jan 15, 2021
Updated on Jan 10, 2021
Updated on Jan 17, 2021
Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.