Due to the wide interest in advanced multimedia experience, free-viewpoint communication is being greatly developed in recent years. In the free-viewpoint communication, viewers can perceive a view from any angle and any position of a scene. Even though the preferred views are not captured, we can generate the views through virtual view synthesis that synthesizes an arbitrary view from captured reference view(s). For daily use, only one or few cameras in baseline distance are given to capture the scene that makes the virtual view synthesis challenging. The task is more difficult when the camera is continuously moving. In this paper, we propose aparticle cellto model a reference view sequence to a set of moving particles for virtual view synthesis. Using our novel hybrid motion estimation scheme, the projected coordinates of particles in each frame are obtained even they are occluded. The particles are warped to a virtual view and synthesized as a virtual view sequence. Our method is applicable for both dynamic camera setting and static camera setting. The experimental results show our method outperforms the state-of-the-art algorithms in dynamic camera datasets and presents improvement in static camera datasets in general.

Depth image-based rendering is an important technique for virtual view synthesis with limited 3-D data. However, occluded areas result in disocclusions in synthesized images. Filling of the disocclusions in a plausible manner is a critical task in virtual view synthesis. In addition to spatial consistency, temporal consistency of the filled regions also affects the visual quality. In this paper, we propose a novel codebook method called sprite cell for filling the disocclusions with high spatial and temporal consistency. Each codeword consists of a color vector, depth value, frame log, and the confidence score of the corresponding pixel. In contrast with the existing methods that reuse the filling results of previous frames without considering their accuracy, the proposed method estimates the confidence scores of the filling results to prevent temporal continuation of filling errors. Moreover, we introduce a method to correct the luminance of filled disocclusions that compensates for the change of scenes. The experimental results show that the proposed method achieves both objective and subjective improvements over the state-of-the-art methods. The sequences synthesized with the proposed method have higher spatio-temporal consistency.