Reconstructing 3D facial shapes from 2D images is critical to face analysis applications. However, it is difficult to get 3D ground truth shapes for face images because of the requirements for special sensors and constrained environment in data acquisition. In order to overcome the scarcity of 3D face datasets, a real-world, large-scale, high-precision multi-modal dataset, namely PixelAI-3DFace, is released for bridge the gap between 2D images and 3D shapes in this challenge.
Input: left, frontal and right side 2D facial images captured by self-customized trinocular high resolution camera.
Output: 106 3D facial landmarks.
1st prize: USD$ 9,000
2nd prize: USD$ 4,000
3rd prize: USD$ 2,000
| Set | Subjects | Images | 3D Scans | 3D Meshes |
|---|---|---|---|---|
| Training Set | 523 | 15,612 | 5,204 | 5,204 |
| Validation Set | 106 | 2,691 | 897 | 897 |
| Testing Set | 225 | 7,128 | 2,376 | 2,376 |
