PhoMoH: Implicit Photorealistic 3D Models of Human Heads
This work addresses the need for realistic 3D human head models in applications like gaming or virtual reality, but it is incremental as it builds upon prior expressive head models.
The authors tackled the problem of creating photorealistic 3D models of human heads with complex features like hair and clothing, achieving this by augmenting an existing head model with neural fields to learn detailed geometry and appearance from relatively little data.
We present PhoMoH, a neural network methodology to construct generative models of photo-realistic 3D geometry and appearance of human heads including hair, beards, an oral cavity, and clothing. In contrast to prior work, PhoMoH models the human head using neural fields, thus supporting complex topology. Instead of learning a head model from scratch, we propose to augment an existing expressive head model with new features. Concretely, we learn a highly detailed geometry network layered on top of a mid-resolution head model together with a detailed, local geometry-aware, and disentangled color field. Our proposed architecture allows us to learn photo-realistic human head models from relatively little data. The learned generative geometry and appearance networks can be sampled individually and enable the creation of diverse and realistic human heads. Extensive experiments validate our method qualitatively and across different metrics.