  While weakly supervised multi-view face reconstruction (MVR) is garnering
increased attention, one critical issue still remains open: how to effectively
fuse multiple image information to reconstruct high-precision 3D models. In
this regard, we propose a novel model called Deep Fusion MVR (DF-MVR) to
reconstruct high-precision 3D facial shapes from multi-view images.
Specifically, we introduce MulEn-Unet, a multi-view encoding to single decoding
framework with skip connections and attention. This design allows for the
extraction, integration, and compensation of deep features with attention from
multi-view images. Furthermore, we adopt the involution kernel to enrich deep
fusion features with channel features. In addition, we develop the face parse
network to learn, identify, and emphasize the critical common face area within
multi-view images. Experiments on Pixel-Face and Bosphorus datasets indicate
the superiority of our model. Without 3D annotation, DF-MVR achieves 5.2% and
3.0% RMSE improvement over the existing weakly supervised MVRs respectively on
Pixel-Face and Bosphorus dataset. Code will be available publicly at
https://github.com/weiguangzhao/DF_MVR.
