Unsupervised Multi-collaborative Learning Network for 3D Face Reconstruction

Monocular image-based 3D fine face reconstruction techniques aim to reconstruct 3D faces with rich face details from a single image. Existing methods have achieved remarkable results, but they cannot accurately extract light and perspective information, resulting in reconstructed faces with poor details and more noise. To this end, we propose a method for 3D face reconstruction using multi-collaborative learning network. Specifically, we design an illumination and view feature extraction network, which combines the ideas of FCN-style [8] point-by-point addition and UNet-style [7] channel dimension splicing and fusion. In this way, features at different scales can be better filtered and integrated, and key semantic information can be extracted, we can make full use of the effective features at different scales to obtain accurate light and view information. In addition, in order to be able to obtain a more comprehensive and realistic albedo characterisation, we propose a multi-resolution co-optimization module. Extensive experimental results on several evaluation datasets show that our method achieves significant improvements and excellent performance compared to state-of-the-art methods.