Name
Abstract | ||
|---|---|---|
ABSTRACT Manually creating realistic, digital human heads is a difficult and time-consuming task for artists. While 3D scanners and photogrammetry allow for quick and automatic reconstruction of heads, finding an actor who fits specific character appearance descriptions can be difficult. Moreover, modern open-world videogames feature several thousands of characters that cannot realistically all be cast and scanned. Therefore, researchers are investigating generative models to create heads fitting a specific character appearance description. While current methods are able to generate believable head shapes quite well, generating a corresponding high-resolution and high-quality texture which respects the character’s appearance description is not possible using current state of the art methods. This work presents a method that generates synthetic face textures under the following constraints: (i) there is no reference photograph to build the texture, (ii) game artists control the generative process by providing precise appearance attributes, the face shape, and the character’s age and gender, and (iii) the texture must be of adequately high resolution and look believable when applied to the given face shape. Our method builds upon earlier deep learning approaches addressing similar problems. We propose several key additions to these methods to be able to use them in our context, specifically for artist control and small training data. In spite of training with a limited amount of training data, just over 100 samples, our model produces realistic textures which comply to a diverse range of skin, hair, lip and iris colors specified through our intuitive description format and augmentation thereof. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3424636.3426898 | MIG |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christian Murphy | 1 | 0 | 0.68 |
| Sudhir P. Mudur | 2 | 201 | 45.52 |
| Daniel Holden | 3 | 176 | 9.75 |
| Marc-André Carbonneau | 4 | 0 | 0.34 |
| Donya Ghafourzadeh | 5 | 0 | 0.34 |
| Andre Beauchamp | 6 | 0 | 0.34 |