Total: 7
Procedurally generating cohesive and interesting game environments is challenging and time-consuming. In order for the relationships between the game elements to be natural, common-sense has to be encoded into arrangement of the elements. In this work, we investigate a machine learning approach for world creation using content from the multi-player text adventure game environment LIGHT (Urbanek et al. 2019). We introduce neural network based models to compositionally arrange locations, characters, and objects into a coherent whole. In addition to creating worlds based on existing elements, our models can generate new game content. Humans can also leverage our models to interactively aid in worldbuilding. We show that the game environments created with our approach are cohesive, diverse, and preferred by human evaluators compared to other machine learning based world construction algorithms.
General Game Playing agents are required to play games they have never seen before simply by looking at a formal description of the rules of the game at runtime. Previous successful agents have been based on search with generic heuristics, with almost no work done into using machine learning. Recent advances in deep reinforcement learning have shown it to be successful in some two-player zero-sum board games such as Chess and Go. This work applies deep reinforcement learning to General Game Playing, extending the AlphaZero algorithm and finds that it can provide competitive results.
AI Planning has been shown to be a useful approach for the generation of narrative in interactive entertainment systems and games. However, the creation of the underlying narrative domain models is challenging: the well documented AI planning modelling bottleneck is further compounded by the need for authors, who tend to be non-technical, to create content. We seek to support authors in this task by allowing natural language (NL) plot synopses to be used as a starting point from which planning domain models can be automatically acquired. We present a solution which analyses input NL text summaries, and builds structured representations from which a pddl model is output (fully automated or author in-the-loop). We introduce a novel sieve-based approach to pronoun resolution that demonstrates consistently high performance across domains. In the paper we focus on authoring of narrative planning models for use in interactive entertainment systems and games. We show that our approach exhibits comprehensive detection of both actions and objects in the system-extracted domain models, in combination with significant improvement in the accuracy of pronoun resolution due to the use of contextual object information. Our results and an expert user assessment show that our approach enables a reduction in authoring effort required to generate baseline narrative domain models from which variants can be built.
Text effect transfer aims at learning the mapping between text visual effects while maintaining the text content. While remarkably successful, existing methods have limited robustness in font transfer and weak generalization ability to unseen effects. To address these problems, we propose FET-GAN, a novel end-to-end framework to implement visual effects transfer with font variation among multiple text effects domains. Our model achieves remarkable results both on arbitrary effect transfer between texts and effect translation from text to graphic objects. By a few-shot fine-tuning strategy, FET-GAN can generalize the transfer of the pre-trained model to the new effect. Through extensive experimental validation and comparison, our model advances the state-of-the-art in the text effect transfer task. Besides, we have collected a font dataset including 100 fonts of more than 800 Chinese and English characters. Based on this dataset, we demonstrated the generalization ability of our model by the application that complements the font library automatically by few-shot samples. This application is significant in reducing the labor cost for the font designer.
Automated story generation is a challenging task which aims to automatically generate convincing stories composed of successive plots correlated with consistent characters. Most recent generation models are built upon advanced neural networks, e.g., variational autoencoder, generative adversarial network, convolutional sequence to sequence model. Although these models have achieved prompting results on learning linguistic patterns, very few methods consider the attributes and prior knowledge of the story genre, especially from the perspectives of explainability and consistency. To fill this gap, we propose a character-centric neural storytelling model, where a story is created encircling the given character, i.e., each part of a story is conditioned on a given character and corresponded context environment. In this way, we explicitly capture the character information and the relations between plots and characters to improve explainability and consistency. Experimental results on open dataset indicate that our model yields meaningful improvements over several strong baselines on both human and automatic evaluations.
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.
Automatic Storytelling has consistently been a challenging area in the field of natural language processing. Despite considerable achievements have been made, the gap between automatically generated stories and human-written stories is still significant. Moreover, the limitations of existing automatic storytelling methods are obvious, e.g., the consistency of content, wording diversity. In this paper, we proposed a multi-pass hierarchical conditional variational autoencoder model to overcome the challenges and limitations in existing automatic storytelling models. While the conditional variational autoencoder (CVAE) model has been employed to generate diversified content, the hierarchical structure and multi-pass editing scheme allow the story to create more consistent content. We conduct extensive experiments on the ROCStories Dataset. The results verified the validity and effectiveness of our proposed model and yields substantial improvement over the existing state-of-the-art approaches.