Neural 3D Face Shape Stylization Based on Single Style Template via Weakly Supervised Learning.

Abstract

3D Face shape stylization refers to transforming a realistic 3D face shape into a different style, such as a cartoon face style. To solve this problem, this paper proposes modeling this task as a deformation transfer problem. This approach significantly reduces labor costs, as the artists would only need to create a single template for each face style. Realistic facial features of the original 3D face e.g. the nose or chin shape, would thus be automatically transferred to those in the style template. Deformation transfer methods, however, have two drawbacks. They are slow and they require re-optimization for every new input face. To address these weaknesses, we propose a neural network-based 3D face shape stylization method. This method is trained through weakly supervised learning, and its template's structure is preserved using our novel templateguided mesh smoothing regularization. Our method is the first learning-based deformation transfer method for 3D face shape stylization. Its employment offers the useful and practical benefit of not requiring paired training data. The experiments show that the quality of the stylized faces obtained by our method is comparable to that of the traditional deformation transfer method, achieving an average Chamfer Distance of approximately 0.01mm. However, our approach significantly boosts the processing speed, achieving a rate approximately 3,000 times faster than the traditional deformation transfer. Project page: https://peizhiyan.github.io/docs/style.