3D to 4D Facial Expressions Generation Guided by Landmarks

• URL: http://arxiv.org/abs/2105.07463v1
• Date: Sun, 16 May 2021 15:52:29 GMT
• Title: 3D to 4D Facial Expressions Generation Guided by Landmarks
• Authors: Naima Otberdout, Claudio Ferrari, Mohamed Daoudi, Stefano Berretti, Alberto Del Bimbo
• Abstract summary: Given one input 3D neutral face, can we generate dynamic 3D (4D) facial expressions from it? We first propose a mesh encoder-decoder architecture (Expr-ED) that exploits a set of 3D landmarks to generate an expressive 3D face from its neutral counterpart. We extend it to 4D by modeling the temporal dynamics of facial expressions using a manifold-valued GAN.
• Score: 35.61963927340274
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While deep learning-based 3D face generation has made a progress recently, the problem of dynamic 3D (4D) facial expression synthesis is less investigated. In this paper, we propose a novel solution to the following question: given one input 3D neutral face, can we generate dynamic 3D (4D) facial expressions from it? To tackle this problem, we first propose a mesh encoder-decoder architecture (Expr-ED) that exploits a set of 3D landmarks to generate an expressive 3D face from its neutral counterpart. Then, we extend it to 4D by modeling the temporal dynamics of facial expressions using a manifold-valued GAN capable of generating a sequence of 3D landmarks from an expression label (Motion3DGAN). The generated landmarks are fed into the mesh encoder-decoder, ultimately producing a sequence of 3D expressive faces. By decoupling the two steps, we separately address the non-linearity induced by the mesh deformation and motion dynamics. The experimental results on the CoMA dataset show that our mesh encoder-decoder guided by landmarks brings a significant improvement with respect to other landmark-based 3D fitting approaches, and that we can generate high quality dynamic facial expressions. This framework further enables the 3D expression intensity to be continuously adapted from low to high intensity. Finally, we show our framework can be applied to other tasks, such as 2D-3D facial expression transfer.

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