As an elastic hosting platform, cloud computing has been attracting many attentions for transferring compute-intensive applications from static self-hosting to flexible cloud-based hosting. Distributed virtual environments (DVEs) which typically involve massive users interacting at the same time and feature significant workload dynamics either in spatial due to in-game user mobility or in temporal due to the fluctuating user population, potentially are suitable applications with cloud-based hosting because of the need of resource elasticity. We explore the dynamic resource management for cloud-based DVEs by taking into account their multi-level workload dynamics which differ them from other applications. Simulation results demonstrates the advantages of our developed methods over existing ones.

With recent advances in distributed virtual worlds, online users have access to larger and more immersive virtual environments. Sometimes the number of users in virtual worlds is not large enough to make the virtual world realistic. In our paper, we present a crowd simulation algorithm that allows a large number of virtual agents to navigate around the virtual world autonomously by sequentially selecting the goals. Our approach is based on our sequential goal selection model (SGS) which can learn goal-selection patterns from synthetic sequences. We demonstrate our algorithm's simulation results in complex scenarios containing more than 20 goals.

We present a data-driven approach to derive part-level segmentation and labeling of free-hand sketches, which depict single objects with multiple parts. Our method performs segmentation and labeling simultaneously, by inferring a structure that best fits the input sketch, through selecting and connecting 3D components in the database. The problem is formulated using Mixed Integer Programming, which optimizes over both the local fitness of the selected components and the global plausibility of the connected structure. Evaluations show that our algorithm is significantly better than the straightforward approaches based on direct retrieval or part assembly, and can effectively handle challenging variations in the sketch.

We present a method to build 3D structured mechanical collages consisting of numerous elements from the database given artist-designed proxy models. The construction is guided by some graphic design principles, namely unity, variety and contrast. Our results are visually more pleasing than previous works as confirmed by a user study.

A stereo infinite panorama is a panoramic image that may be infinitely extended by continuously stitching together stereo images that depict similar scenes, but are taken from different geographic locations. It can be used to create interesting walkthrough environment. An important issue underlying this application is to seamlessly stitch two stereo images together. Although many methods have been proposed for stitching 2D images, they may not work well on stereo images, due to the difficulty in ensuring disparity consistency. In this paper, we propose a novel method to stitch two stereo images seamlessly. We first apply the graph cut algorithm to compute a seam for stitching, with a novel disparity-aware energy function to both ensure disparity continuity and suppress visual artifacts around the seam. We then apply a modified warping-based disparity scaling algorithm to suppress the seam in depth domain. Experiments show that our stitching method is capable of producing high quality stereo infinite panoramas.