With the rapid growth in the number and quality of mesh animations, browsing mesh animations wastes considerable bandwidth and requires tremendous rendering resources. Accordingly, the preview technique, which provides users a rapid understanding of a mesh animation before downloading, has received increasing attention. In this paper, we propose an automatic mesh animation preview method that incorporates the interframe motion saliency, intraframe surface saliency, user preference, and camera smoothness constraint to formulate the viewpoint selection as a minimization problem. Then, the minimization is solved by finding the shortest path, and the viewpoints for mesh animation preview are generated accordingly. A voting mechanism is introduced into this process to collect user feedbacks and periodically use user voting feedbacks to refine the preview camera path. The experiment results show that our mesh preview method helps users acquire a good understanding of animation contents. A user study demonstrates that our preview results are superior to those generated by typical preview methods in terms of the subjective visual quality.

Rendering large-scale 3-D scenes on a thin client is attracting increasing attention with the development of the mobile Internet. Efficient scene prefetching to provide timely data with a limited cache is one of the most critical issues for remote 3-D data scheduling in networked virtual environment applications. Existing prefetching schemes predict the future positions of each individual user based on user traces. In this paper, we investigate scene content sequences accessed by various users instead of user viewpoint traces and propose a user access pattern-based 3-D scene prefetching scheme. We make a relationship graph-based clustering to partition history user access sequences into several clusters and choose representative sequences from among these clusters as user access patterns. Then, these user access patterns are prioritized by their popularity and users' personal preference. Based on these access patterns, the proposed prefetching scheme predicts the scene contents that will most likely be visited in the future and delivers them to the client in advance. The experiment results demonstrate that our user access pattern-based prefetching approach achieves a high hit ratio and outperforms the prevailing prefetching schemes in terms of access latency and cache capacity.