MUNCH: Modelling Unique 'N Controllable Heads
Debayan Deb Suvidha Tripathi Pranit Puri
The automated generation of 3D human heads has been an intriguing and
challenging task for computer vision researchers. Prevailing methods synthesize
realistic avatars but with limited control over the diversity and quality of
rendered outputs and suffer from limited correlation between shape and texture
of the character. We propose a method that offers quality, diversity, control,
and realism along with explainable network design, all desirable features to
game-design artists in the domain. First, our proposed Geometry Generator
identifies disentangled latent directions and generate novel and diverse
samples. A Render Map Generator then learns to synthesize multiply high-fidelty
physically-based render maps including Albedo, Glossiness, Specular, and
Normals. For artists preferring fine-grained control over the output, we
introduce a novel Color Transformer Model that allows semantic color control
over generated maps. We also introduce quantifiable metrics called Uniqueness
and Novelty and a combined metric to test the overall performance of our model.
Demo for both shapes and textures can be found:
https://munch-seven.vercel.app/. We will release our model along with the
synthetic dataset.