This study aims to explore game backgrounds and content generation using motion graphics. Motion graphics can be utilized to generate contents in various fields including games, cartoons, movies, animations, characters, advertisements, etc. This study identifies how game backgrounds and game scenes are simply generated by utilizing motion graphics. And synthesizing techniques are used to generate game backgrounds and game scenes. Also, for game backgrounds and characters, motion graphics are applied to simply express special effects such as various flames, blazes, thunderbolts, etc. On the subject of secondary school students, this study evaluates their game background and character generation competencies with two methods to develop game industry and improve content personnel. Evaluation results of 88 points and 85 points are shown from the evaluation of the last results for evaluation items according to the standard of 100 points, and it suggests that game development personnel and character content production personnel be reserved with professional education and support in the future.

In this paper, firstly, acceleration-time histories were generated by varying strong motion duration in the frequency domain for application to a seismically isolated nuclear power structure, so as to examine the effects of strong motion duration on the behavior of the structure. Secondly, real recorded earthquakes were modified to match the target response spectrum based on the revised SRP 3.7.1(2007) and the modified time histories were applied to the analysis of a seismically isolated nuclear power structure. The obtained values of acceleration and displacement responses of the structure were, finally, compared with the values obtained in case of applying acceleration-time histories generated in the frequency domain to the structure.

The purpose of this study is to develop a motion generation technique based on a double inverted pendulum model (DIPM) that learns and reproduces humanoid robot (or virtual human) motions while keeping its balance in a pattern similar to a human. DIPM consists of a cart and two inverted pendulums, connected in a serial. Although the structure resembles human upper- and lower-body, the balancing motion in DIPM is different from the motion that human does. To do this, we use the motion capture data to obtain the reference motion to keep the balance in the existence of external force. By an optimization technique minimizing the difference between the motion of DIPM and the reference motion, control parameters of the proposed method were learned in advance. The learned control parameters are re-used for the control signal of DIPM as input of linear quadratic regulator that generates a similar motion pattern as the reference. In order to verify this, we use virtual human experiments were conducted to generate the motion that naturally balanced.

본 논문에서는 사용자의 대응정보를 반영하여 소스 캐릭터와 다른 골격을 가진 타깃 캐릭터의 움직임을 생성하는 방법에 대하여 제안한다. 본 시스템을 통해 사용자는 소스 캐릭터의 제어할 부위와 타깃 캐릭터의 제어될 부위를 대응하여 타깃 캐릭터의 움직임을 생성할 수 있다. 우리는 골격에 제한 없이 타깃 캐릭터의 자세생성을 위해 대응자세의 쌍을 예제로 이용한다. 그리고 뼈의 수에 상관없이 자유롭게 관절의 대응을 제공하기 위해 방향벡터를 사용하여 관절의 구조를 간략화 한다. 최종적인 자세는 예제들의 가중치 합을 통해 생성된다. 본 논문의 실험적 결과를 통해 시스템이 실시간으로 골격이 다른 타깃 캐릭터의 기본적인 움직임을 생성하면서 또한 사용자가 지정한 부위의 외형적 움직임을 생성할 수 있음을 보인다.

People have expected a humanoid robot to move as naturally as a human being does. The natural movements of humanoid robot may provide people with safer physical services and communicate with persons through motions more correctly. This work presented a methodology to generate the natural motions for a humanoid robot, which are converted from human motion capture data. The methodology produces not only kinematically mapped motions but dynamically mapped ones. The kinematical mapping reflects the human-likeness in the converted motions, while the dynamical mapping could ensure the movement stability of whole body motions of a humanoid robot. The methodology consists of three processes: (a) Human modeling, (b) Kinematic mapping and (c) Dynamic mapping. The human modeling based on optimization gives the ZMP (Zero Moment Point) and COM (Center of Mass) time trajectories of an actor. Those trajectories are modified for a humanoid robot through the kinematic mapping. In addition to modifying the ZMP and COM trajectories, the lower body (pelvis and legs) motion of the actor is then scaled kinematically and converted to the motion available to the humanoid robot considering dynamical aspects. The KIST humanoid robot, Mahru, imitated a dancing motion to evaluate the methodology, showing the good agreement in the motion.