Face Triangulation for Graphic Design

Nowadays low poly has emerged as a popular element in graphic design, creating sculpture/crystal-like effects. Various movie posters utilize this technique to highlight characteristics of the main subject, e.g. the Star Wars poster shown below. And multiple mobile apps aim to make this visual effect more approachable for common users.

Image Credit: Star Wars low poly portraits, designed by Vladan Filipovic

To convert a posed photo to low poly style, designers employ different tools and emphasize different aspects. Among all these variants, a typical production process involves three steps: 1. place vertices (usually 500~5000) on suitable locations of image; 2. generate mesh from vertices using Delaunay triangulation; 3. fuse colors inside each triangle-coverage area.

The last two steps are quite straightforward and there are several off-the-shelf tools for them. What takes most effort and exhibits unique aesthetic taste is the first step, i.e. choosing the right locations on image to place vertices. So in the post, we attempt to automate this vertices selection step by referring to computer vision approaches, especially in face triangulation domain.

Generally two principles define a good set of vertices: 1. vertices should be distributed more or less uniformly, so that the shear of each triangle generated wouldn't be dramatic; 2. On the boundaries of semantic parts or textured details, vertices should be more densely, such that unions of triangles would correspond to semantic parts. In this sense, the problem has been cast onto seeking a proper probability density map for sampling.

Here we advocate to combine face alignment and boundary detection to produce a desired density map. Dense facial landmarks will place more emphasize on semantic facial parts (e.g. eyebrows, eyes, nose, mouth etc.) and edge maps will preserve object contours and fine details in image. My lovely Alice and two dear professors are taken as models and the final effect is illustrated as follows.

Image Credit: Prof. Xiaoou Tang & Prof. Xiaogang Wang

The current result seems surreal but nice. Future improvements could be made on two directions: 1. Devise post-processing method to re-split and re-merge triangles according to established scale distribution; 2. Form an end-to-end system for learning point correspondences, just like Pointer Networks.