Computer graphics techniques for modeling page turning

Turning the page is a mechanical part of the cognitive act of reading that we do literally unthinkingly. Interest in realistic book models for digital libraries and other online documents is growing. Yet actually producing a computer graphics implementation for modeling page turning is a challenging undertaking. There are many possible foundations: two-dimensional models that use reflection and rotation; geometrical models using cylinders or cones; mass-spring models that simulate the mechanical properties of paper at varying degrees of fidelity; finite-element models that directly compute the actual forces within a piece of paper. Even the simplest methods are not trivial, and the more sophisticated ones involve detailed physical and mathematical models. The variety, intricacy and complexity of possible ways of simulating this fundamental act of reading is virtually unknown. This paper surveys computer graphics models for page turning. It combines a tutorial introduction that covers the range of possibilities and complexities with a mathematical synopsis of each model in sufficient detail to serve as a basis for implementation. Illustrations are included that are generated by our implementations of each model. The techniques presented include geometric methods (both two- and three-dimensional), mass-spring models with varying degrees of accuracy and complexity, and finite-element models. We include a detailed comparison of experimentally-determined computation time and subjective visual fidelity for all methods discussed. The simpler techniques support convincing real-time implementations on ordinary workstations

LDAExplore: Visualizing Topic Models Generated Using Latent Dirichlet Allocation

We present LDAExplore, a tool to visualize topic distributions in a given document corpus that are generated using Topic Modeling methods. Latent Dirichlet Allocation (LDA) is one of the basic methods that is predominantly used to generate topics. One of the problems with methods like LDA is that users who apply them may not understand the topics that are generated. Also, users may find it difficult to search correlated topics and correlated documents. LDAExplore, tries to alleviate these problems by visualizing topic and word distributions generated from the document corpus and allowing the user to interact with them. The system is designed for users, who have minimal knowledge of LDA or Topic Modelling methods. To evaluate our design, we run a pilot study which uses the abstracts of 322 Information Visualization papers, where every abstract is considered a document. The topics generated are then explored by users. The results show that users are able to find correlated documents and group them based on topics that are similar

Tex2Shape: Detailed Full Human Body Geometry From a Single Image

We present a simple yet effective method to infer detailed full human body shape from only a single photograph. Our model can infer full-body shape including face, hair, and clothing including wrinkles at interactive frame-rates. Results feature details even on parts that are occluded in the input image. Our main idea is to turn shape regression into an aligned image-to-image translation problem. The input to our method is a partial texture map of the visible region obtained from off-the-shelf methods. From a partial texture, we estimate detailed normal and vector displacement maps, which can be applied to a low-resolution smooth body model to add detail and clothing. Despite being trained purely with synthetic data, our model generalizes well to real-world photographs. Numerous results demonstrate the versatility and robustness of our method