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3D face reconstruction from a single image is a task that has garneredincreased interest in the Computer Vision community, especially due to itsbroad use in a number of applications such as realistic 3D avatar creation,pose invariant face recognition and face hallucination. Since the introductionof the 3D Morphable Model in the late 90's, we witnessed an explosion ofresearch aiming at particularly tackling this task. Nevertheless, despite theincreasing level of detail in the 3D face reconstructions from single imagesmainly attributed to deep learning advances, finer and highly deformablecomponents of the face such as the tongue are still absent from all 3D facemodels in the literature, although being very important for the realness of the3D avatar representations. In this work we present the first, to the best ofour knowledge, end-to-end trainable pipeline that accurately reconstructs the3D face together with the tongue. Moreover, we make this pipeline robust in"in-the-wild" images by introducing a novel GAN method tailored for 3D tonguesurface generation. Finally, we make publicly available to the community thefirst diverse tongue dataset, consisting of 1,800 raw scans of 700 individualsvarying in gender, age, and ethnicity backgrounds. As we demonstrate in anextensive series of quantitative as well as qualitative experiments, our modelproves to be robust and realistically captures the 3D tongue structure, even inadverse "in-the-wild" conditions.

3D human tongue reconstruction from single "in-the-wild" images