Unsupervised Learning Facial Parameter Regressor for Action Unit Intensity Estimation via Differentiable Renderer

Facial action unit (AU) intensity is an index to describe all visually discernible facial movements. Most existing methods learn intensity estimator with limited AU data, while they lack generalization ability out of the dataset. In this paper, we present a framework to predict the facial parameters (including identity parameters and AU parameters) based on a bone-driven face model (BDFM) under different views. The proposed framework consists of a feature extractor, a generator, and a facial parameter regressor. The regressor can fit the physical meaning parameters of the BDFM from a single face image with the help of the generator, which maps the facial parameters to the game-face images as a differentiable renderer. Besides, identity loss, loopback loss, and adversarial loss can improve the regressive results. Quantitative evaluations are performed on two public databases BP4D and DISFA, which demonstrates that the proposed method can achieve comparable or better performance than the state-of-the-art methods. What's more, the qualitative results also demonstrate the validity of our method in the wild

Fast and Robust Face-to-Parameter Translation for Game Character Auto-Creation

With the rapid development of Role-Playing Games (RPGs), players are now allowed to edit the facial appearance of their in-game characters with their preferences rather than using default templates. This paper proposes a game character auto-creation framework that generates in-game characters according to a player's input face photo. Different from the previous methods that are designed based on neural style transfer or monocular 3D face reconstruction, we re-formulate the character auto-creation process in a different point of view: by predicting a large set of physically meaningful facial parameters under a self-supervised learning paradigm. Instead of updating facial parameters iteratively at the input end of the renderer as suggested by previous methods, which are time-consuming, we introduce a facial parameter translator so that the creation can be done efficiently through a single forward propagation from the face embeddings to parameters, with a considerable 1000x computational speedup. Despite its high efficiency, the interactivity is preserved in our method where users are allowed to optionally fine-tune the facial parameters on our creation according to their needs. Our approach also shows better robustness than previous methods, especially for those photos with head-pose variance. Comparison results and ablation analysis on seven public face verification datasets suggest the effectiveness of our method