Towards High-fidelity Nonlinear 3D Face Morphable Model (1904.04933v1)

Abstract: 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.

High-Fidelity Nonlinear 3D Face Morphable Models: An Advanced Approach

The development of 3D face reconstruction has become a fundamental pursuit in the realms of computer vision and computer graphics. This paper, by Tran, Liu, and Liu, sets forth a refined model for 3D face morphable modeling that accentuates high-fidelity reconstruction. The primary aim is to circumvent the constraints hampering the realization of detailed models through the utilization of non-linear modeling and innovative network architectures.

The authors identify a prevalent issue in embedding 3D morphable basis functions into deep neural networks, where excessive regularization thwarts the ability to learn models with high fidelity. Such regularizations are typically employed to handle ambiguities in data, yet they concurrently stifle the model's ability to resolve intricate details. This paper propounds a superior approach to learn 3D morphable models by introducing additional shape and albedo proxies. These proxies facilitate the alleviation of regularizations, thereby fostering a comprehensive capture of shape and albedo characteristics.

In a bid to enhance the learning process, the dual-pathway network architecture is devised. This design equilibrates the capabilities of global and local-based models, bridging the gap between robustness and detail orientation. The dual-path mechanism employs a global pathway for capturing global face structures while several local pathways target specific facial elements, thereby improving overall detail and computational efficiency.

The proposed methodology demonstrates a marked improvement in the ability to delineate the nuances of facial features compared to prior linear and non-linear methodologies. The integration of dual-pathway networks and proxy learning resolves the conflicting requirements of shape and albedo regularizations, ensuring an accurate high-fidelity reconstruction.

Empirical evaluations reveal substantial advancements in texture representation ability when tested on datasets such as AFLW2000-3D, manifesting reduced reconstruction error and heightened realism in synthesis. This capacity to recover nuanced facial texture and geometry is significantly enhanced with the introduction of the global-local-based architecture and proxy use. Furthermore, image reconstruction fidelity is evidenced quantitatively and qualitatively, even under occlusion, thanks to novel architectural choices like the residual soft symmetry loss.

Moreover, the paper explores identity preservation aspects of face reconstruction, measuring distances in the identity feature space through pretrained face recognition networks and demonstrating superior performance in maintaining distinct facial characteristics. The capacity to manipulate facial components allows applications in facial editing and relighting, underscoring the model's utility beyond mere reconstruction.

In conclusion, this paper sheds light on the critical impediments to developing high-fidelity 3D face morphable models and provides a comprehensive solution by refining learning objectives and architectures. By allowing for detailed and realistic reconstructions through the proposed innovations, this work sets a foundation for further advancements in AI-driven face modeling technology. Researchers in the field may extrapolate these findings for practical applications such as animation, healthcare, and bespoke virtual environments, paving the way for more authentic human-computer interfaces in future developments.