In this work, we propose a stroke-based hairstyle editing network, dubbed HairstyleNet, allowing users to conveniently change the hairstyles of an image in an interactive fashion. Different from previous works, we simplify the hairstyle editing process where users can manipulate local or entire hairstyles by adjusting the parameterized hair regions. Our HairstyleNet consists of two stages: a stroke parameterization stage and a stroke-to-hair generation stage. In the stroke parameterization stage, we first introduce parametric strokes to approximate the hair wisps, where the stroke shape is controlled by a quadratic Bézier curve and a thickness parameter. Since rendering strokes with thickness to an image is not differentiable, we opt to leverage a neural renderer to construct the mapping from stroke parameters to a stroke image. Thus, the stroke parameters can be directly estimated from hair regions in a differentiable way, enabling us to flexibly edit the hairstyles of input images. In the stroke-to-hair generation stage, we design a hairstyle refinement network that first encodes coarsely composed images of hair strokes, face, and background into latent representations and then generates high-fidelity face images with desirable new hairstyles from the latent codes. Extensive experiments demonstrate that our HairstyleNet achieves state-of-the-art performance and allows flexible hairstyle manipulation.

Game character customization is one of the core features of many recent Role-Playing Games (RPGs), where players can edit the appearance of their in-game characters with their preferences. This paper studies the problem of automatically creating in-game characters with a single photo. In recent literature on this topic, neural networks are introduced to make game engine differentiable and the self-supervised learning is used to predict facial customization parameters. However, in previous methods, the expression parameters and facial identity parameters are highly coupled with each other, making it difficult to model the intrinsic facial features of the character. Besides, the neural network based renderer used in previous methods is also difficult to be extended to multi-view rendering cases. In this paper, considering the above problems, we propose a novel method named "PokerFace-GAN" for neutral face game character auto-creation. We first build a differentiable character renderer which is more flexible than the previous methods in multi-view rendering cases. We then take advantage of the adversarial training to effectively disentangle the expression parameters from the identity parameters and thus generate player-preferred neutral face (expression-less) characters. Since all components of our method are differentiable, our method can be easily trained under a multi-task self-supervised learning paradigm. Experiment results show that our method can generate vivid neutral face game characters that are highly similar to the input photos. The effectiveness of our method is verified by comparison results and ablation studies.

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 of 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.

How to manage, store, and index large numbers of videos is an urgent problem to be solved. Although there are many video summarization models achieving good results, models based on low-level features cannot summarize important semantic information and models based on semantic analysis need related text descriptions that do not exist for most videos. As a consequence, the mining semantic information contained in the video itself is a more feasible way. In this paper, we propose an action parsing-driven video summarization model based on reinforcement learning. The model is mainly divided into two parts, video cut by action parsing and video summarization based on reinforcement learning. In the first part, a sequential multiple instance learning model is trained with weakly annotated data to solve the problem of full annotation’s time consuming and weak annotation’s ambiguity. In the second part, we design a deep recurrent neural network-based video summarization model that selects the most distinguishable frames comparing with other actions. Meanwhile, the quality of the extracted key frames could be evaluated by the categorization accuracy. Experiments and comparison with state-of-the-art methods demonstrate the advantage of the proposed approach.

Visual separability between different objects in various image classification tasks is highly uneven. As a consequence, humans need different levels of detailed descriptions to separate objects in multi-granularity similarities. Meanwhile, deep networks, such as convolutional neural networks (C-NNs) have demonstrated great ability in multilevel representations for an object. Unfortunately, existing methods with deep networks in classification typically use the output of the last layer as the only feature to train flat N-way classifiers, which fail to fit the multi-granularity character. In this paper, by regarding different CNN layers as multiple levels of abstraction, we propose a deep decision tree (DDT) to distinguish objects sharing great appearance similarities with utilizing features in all layers. First, deep features in multiple layers are extracted from deep networks as the input for building a DDT. Next, in the training phase, the features from earlier layers are selected for splitting on a deeper node. Finally, multiple DDTs are bagged to make the final prediction by taking the majority vote. The experimental results in two datasets show DDT can greatly improve the classification accuracy in multi-grained tasks than flat models.