i3DMM: Deep Implicit 3D Morphable Model of Human Heads
Tarun Yenamandra, Ayush Tewari, Florian Bernard, Hans-Peter Seidel,, Mohamed Elgharib, Daniel Cremers, Christian Theobalt

TL;DR
The paper introduces i3DMM, a novel deep implicit 3D head model that captures full head geometry, including hair, and enables semantic editing and texture transfer, trained on a new dataset of diverse head scans.
Contribution
It is the first full head morphable model including hair, trained on rigid scans without complex registration, with a novel architecture for disentangling shape and color components.
Findings
Outperforms state-of-the-art models in head modeling tasks.
Enables semantic head editing and texture transfer.
Demonstrates effective disentanglement of geometry and color.
Abstract
We present the first deep implicit 3D morphable model (i3DMM) of full heads. Unlike earlier morphable face models it not only captures identity-specific geometry, texture, and expressions of the frontal face, but also models the entire head, including hair. We collect a new dataset consisting of 64 people with different expressions and hairstyles to train i3DMM. Our approach has the following favorable properties: (i) It is the first full head morphable model that includes hair. (ii) In contrast to mesh-based models it can be trained on merely rigidly aligned scans, without requiring difficult non-rigid registration. (iii) We design a novel architecture to decouple the shape model into an implicit reference shape and a deformation of this reference shape. With that, dense correspondences between shapes can be learned implicitly. (iv) This architecture allows us to semantically…
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Taxonomy
TopicsFace recognition and analysis · 3D Shape Modeling and Analysis · Generative Adversarial Networks and Image Synthesis
