Virtual reality (VR) allows users to explore and experience a computer-simulated virtual environment so that VR users can be immersed in a totally artificial virtual world and interact with arbitrary virtual objects. However, the limited physical tracking space usually restricts the exploration of large virtual spaces, and VR users have to use special locomotion techniques to move from one location to another. Among these techniques, redirected walking (RDW) is one of the most natural locomotion techniques to solve the problem based on near-natural walking experiences. The core idea of the RDW technique is to imperceptibly guide users on virtual paths, which might vary from the paths they physically walk in the real world. In a similar way, some RDW algorithms imperceptibly change the structure and layout of the virtual environment such that the virtual environment fits into the tracking space. In this survey, we first present a taxonomy of existing RDW work. Based on this taxonomy, we compare and analyze both contributions and shortcomings of the existing methods in detail, and find view manipulation methods offer satisfactory visual effect but the experience can be interrupted when users reach the physical boundaries, while virtual environment manipulation methods can provide users with consistent movement but have limited application scenarios. Finally, we discuss possible future research directions, indicating combining artificial intelligence with this area will be effective and intriguing.

Virtual reality (VR) offers an artificial, com-puter generated simulation of a real life environment. It originated in the 1960s and has evolved to provide increasing immersion, interactivity, imagination, and intelligence. Because deep learning systems are able to represent and compose information at various levels in a deep hierarchical fashion, they can build very powerful models which leverage large quantities of visual media data. Intelligence of VR methods and applications has been significantly boosted by the recent developmentsin deep learning techniques. VR content creationand exploration relates to image and video analysis, synthesis and editing, so deep learning methods such as fully convolutional networks and general adversarial networks are widely employed, designed specifically to handle panoramic images and video and virtual 3D scenes. This article surveys recent research that uses such deep learning methods for VR content creation and exploration. It considers the problems involved, and discusses possible future directions in this active and emerging research area.

We introduce ShadowGAN, a generative adversarial network (GAN) for synthesizing shadows for virtual objects inserted in images. Given a target image containing several existing objects with shadows, and an input source object with a specified insertionposition, the network generates a realistic shadow for the source object. The shadow is synthesized by a generator; using the proposed local adversarial and global adversarial discriminators, the synthetic shadow’s appearance is locally realistic in shape, and globally consistent with other objects’ shadows in terms of shadow direction and area. To overcome the lack of training data, we produced training samples based on public 3D models and rendering technology. Experimental results from a user study show that the synthetic shadowed results look natural and authentic.