3D to 4D Facial Expressions Generation Guided by Landmarks

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.

Inner Eye Canthus Localization for Human Body Temperature Screening

In this paper, we propose an automatic approach for localizing the inner eye canthus in thermal face images. We first coarsely detect 5 facial keypoints corresponding to the center of the eyes, the nosetip and the ears. Then we compute a sparse 2D-3D points correspondence using a 3D Morphable Face Model (3DMM). This correspondence is used to project the entire 3D face onto the image, and subsequently locate the inner eye canthus. Detecting this location allows to obtain the most precise body temperature measurement for a person using a thermal camera. We evaluated the approach on a thermal face dataset provided with manually annotated landmarks. However, such manual annotations are normally conceived to identify facial parts such as eyes, nose and mouth, and are not specifically tailored for localizing the eye canthus region. As additional contribution, we enrich the original dataset by using the annotated landmarks to deform and project the 3DMM onto the images. Then, by manually selecting a small region corresponding to the eye canthus, we enrich the dataset with additional annotations. By using the manual landmarks, we ensure the correctness of the 3DMM projection, which can be used as ground-truth for future evaluations. Moreover, we supply the dataset with the 3D head poses and per-point visibility masks for detecting self-occlusions. The data will be publicly released.

A Sparse and Locally Coherent Morphable Face Model for Dense Semantic Correspondence Across Heterogeneous 3D Faces

The 3D Morphable Model (3DMM) is a powerful statistical tool for representing 3D face shapes. To build a 3DMM, a training set of face scans in full point-to-point correspondence is required, and its modeling capabilities directly depend on the variability contained in the training data. Thus, to increase the descriptive power of the 3DMM, establishing a dense correspondence across heterogeneous scans with sufficient diversity in terms of identities, ethnicities, or expressions becomes essential. In this manuscript, we present a fully automatic approach that leverages a 3DMM to transfer its dense semantic annotation across raw 3D faces, establishing a dense correspondence between them. We propose a novel formulation to learn a set of sparse deformation components with local support on the face that, together with an original non-rigid deformation algorithm, allow the 3DMM to precisely fit unseen faces and transfer its semantic annotation. We extensively experimented our approach, showing it can effectively generalize to highly diverse samples and accurately establish a dense correspondence even in presence of complex facial expressions. The accuracy of the dense registration is demonstrated by building a heterogeneous, large-scale 3DMM from more than 9,000 fully registered scans obtained by joining three large datasets together.