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We present an approach for the reconstruction of textured 3D meshes of humanheads from one or few views. Since such few-shot reconstruction isunderconstrained, it requires prior knowledge which is hard to impose ontraditional 3D reconstruction algorithms. In this work, we rely on the recentlyintroduced 3D representation $\unicode{x2013}$ neural implicit functions$\unicode{x2013}$ which, being based on neural networks, allows to naturallylearn priors about human heads from data, and is directly convertible totextured mesh. Namely, we extend NeuS, a state-of-the-art neural implicitfunction formulation, to represent multiple objects of a class (human heads inour case) simultaneously. The underlying neural net architecture is designed tolearn the commonalities among these objects and to generalize to unseen ones.Our model is trained on just a hundred smartphone videos and does not requireany scanned 3D data. Afterwards, the model can fit novel heads in the few-shotor one-shot modes with good results.

Multi-NeuS: 3D Head Portraits from Single Image with Neural Implicit Functions