Abstract In virtual reality (VR) applications, the contents are usually generated by creating a 360° Video panorama of a real‐world scene. Although many capture devices are being released, getting high‐resolution panoramas and displaying a virtual world in real‐time remains challenging due to its computationally demanding nature. In this paper, we propose a real‐time 360° Video foveated stitching framework, that renders the entire scene in different level of detail, aiming to create a high‐resolution panoramic Video in real‐time that can be streamed directly to the client. Our foveated stitching algorithm takes Videos from multiple cameras as input, combined with measurements of human visual attention (i.e. the acuity map and the saliency map), can greatly reduce the number of pixels to be processed. We further parallelize the algorithm using GPU to achieve a responsive interface and validate our results via a user study. Our system accelerates graphics computation by a factor of 6 on a Google Cardboard display.

Online bookstores have highly thrived and changed consumer behaviors in these years. However, most customers go to online bookstores only when they have specific targets. One reason is that the current web interfaces are usually too complex and cluttered for users to browse. In addition, current visualization interfaces only display the results associated with a single attribute, thus requiring users to interact intensively to find their targets. Inspired by the user experiences (UX) in physical bookstores, we present Bookwall, an online bookstore interface which comprises two components: Category Map and Wall View, enabling users to find their targets more efficiently and releasing users from the burden of complicated operations. Specifically, the category map produces a map with a "natural" map-like look, providing an overview of the clusters and neighborhood of book categories. The wall view enables displaying query results satisfying dual query attributes simultaneously. The results show that Bookwall can provide the users a favourable alternative visualization.

In this paper, an inter-video mapping approach is proposed to integrate video footages from two dashcams installed on a preceding and its following vehicle to provide the illusion that the driver of the following vehicle can see-through the preceding one. The key challenge is to adapt the perspectives of the two videos based on a small number of common features since a large portion of the common region in the video captured by the following vehicle is occluded by the preceding one. Inspired by the observation that images with the most similar viewpoints yield dense and high-quality matches, the proposed inter-video mapping estimates spatially-varying motions across two videos utilizing images of very similar contents. Specifically, we estimate frame-to-frame motions of each two consecutive images and incrementally add new views into a merged representation. In this way, long-range motion estimation is achieved, and the observed perspective discrepancy between the two videos can be well approximated our motion estimation. Once the inter-video mapping is established, the correspondences can be updated incrementally, so the proposed method is suitable for on-line applications. Our experiments demonstrate the effectiveness of our approach on real-world challenging videos demonstrate the effectiveness of our approach.

A person's handwriting appears differently within a typical range of variations, and the shapes of handwriting characters also show complex interaction with their nearby neighbors. This makes automatic synthesis of handwriting characters and paragraphs very challenging. In this paper, we propose a method for synthesizing handwriting texts according to a writer's handwriting style. The synthesis algorithm is composed by two phases. First, we create the multidimensional morphable models for different characters based on one writer's data. Then, we compute the cursive probability to decide whether each pair of neighboring characters are conjoined together or not. By jointly modeling the handwriting style and conjoined property through a novel trajectory optimization, final handwriting words can be synthesized from a set of collected samples. Furthermore, the paragraphs’ layouts are also automatically generated and adjusted according to the writer's style obtained from the same dataset. We demonstrate that our method can successfully synthesize an entire paragraph that mimic a writer's handwriting using his/her collected handwriting samples.