We propose a method that generates a virtual camera layout of a 3D animation scene by following the cinematic intention of a reference video. From a reference video, cinematic features such as the start frame, end frame, framing, camera movement, and the visual features of the subjects are extracted automatically. The extracted information is used to generate the virtual camera layout, which resembles the camera layout of the reference video. Our method handles stylized as well as human characters with body proportions different from those of humans. We demonstrate the effectiveness of our approach with various reference videos and 3D animation scenes. The user evaluation results show that the generated layouts are comparable to layouts created by the artist, allowing us to assert that our method can provide effective assistance to both novice and professional users when positioning a virtual camera.

Abstract When a person is located between a display and an operating projector, a shadow is cast on the display. The shadow on the display may eliminate important visual information and therefore adversely affect the viewing experiences. There have been various attempts to remove the human shadow cast on a projection display by using multiple projectors. While previous approaches successfully removed the shadow region when a person moderately moves around or stands stationary in front of the display, there is still an afterimage effect due to the lack of consideration of the limb motion of the person. We propose a new real‐time approach to removing the shadow cast by a person who dynamically interacts with the display, making limb motions in a front projection system. The proposed method utilizes a human skeleton obtained from a depth camera to track the posture of the person which changes over time. A model that consists of spheres and conical frustums is constructed based on the skeleton information in order to represent volumetric information of the person being tracked. Our method precisely estimates the shadow region by projecting the volumetric model onto the display. In addition, employment of intensity masks that are built based on a distance field helps suppress the afterimage of the shadow that appears when the person moves abruptly. It also helps blend the projected overlapping images from different projectors and show one smoothly combined display. The experiment results verify that our approach removes the shadow of a person effectively in a front projection environment and is fast enough to achieve real‐time performance.