arXiv:2201.09061
공식 코드 · Papers with Code
Explore the Expression: Facial Expression Generation using Auxiliary Classifier Generative Adversarial Network
원문 초록
Facial expressions are a form of non-verbal communication that humans perform seamlessly for meaningful transfer of information. Most of the literature addresses the facial expression recognition aspect however, with the advent of Generative Models, it has become possible to explore the affect space in addition to mere classification of a set of expressions. In this article, we propose a generative model architecture which robustly generates a set of facial expressions for multiple character identities and explores the possibilities of generating complex expressions by combining the simple ones.
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Disentangled Source-Free Personalization for Facial Expression Recognition with Neutral Target Data
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Biased Heritage: How Datasets Shape Models in Facial Expression Recognition
Navigating Label Ambiguity for Facial Expression Recognition in the Wild
Unimodal and Multimodal Static Facial Expression Recognition for Virtual Reality Users with EmoHeVRDB
QCS: Feature Refining from Quadruplet Cross Similarity for Facial Expression Recognition
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