Towards High-fidelity Nonlinear 3D Face Morphable Model
This work improves 3D face reconstruction for computer vision applications, but it is incremental as it builds on existing nonlinear morphable models.
The paper tackled the problem of learning high-fidelity 3D face morphable models from image collections by addressing regularization-induced ambiguities, resulting in a model that captures higher details and achieves state-of-the-art performance in 3D face reconstruction.
Embedding 3D morphable basis functions into deep neural networks opens great potential for models with better representation power. However, to faithfully learn those models from an image collection, it requires strong regularization to overcome ambiguities involved in the learning process. This critically prevents us from learning high fidelity face models which are needed to represent face images in high level of details. To address this problem, this paper presents a novel approach to learn additional proxies as means to side-step strong regularizations, as well as, leverages to promote detailed shape/albedo. To ease the learning, we also propose to use a dual-pathway network, a carefully-designed architecture that brings a balance between global and local-based models. By improving the nonlinear 3D morphable model in both learning objective and network architecture, we present a model which is superior in capturing higher level of details than the linear or its precedent nonlinear counterparts. As a result, our model achieves state-of-the-art performance on 3D face reconstruction by solely optimizing latent representations.