With the recent advent of Metaverse, the character industry that reflects the characteristics of users' faces is drawing attention. there is a hassle that users have to select face components such as eyes, nose, and mouth one by one. In this paper, we propose a diffusion-based model that automatically generates characters from content human photographs. Our model generates user artistic characters by reflecting content information such as face angle, direction, and shape of a content human photo. In particular, our model automatically analyzes detailed information such as glasses and whiskers from content photo images and reflects them in artistic characters generated. Our network generates the final character through a three-step: diffusion process, UNet, and denoising processes. We use image encoders and CLIP encoders for the connection between style and input data. In the diffusion process, a collection of noise vectors is gradually added to a style vector to enable lossless learning of the detailed styles. All input values except for the style images are vectorized with CLIP encoders and then learned with noise style vectors in the UNet. Subsequently, noise is removed from the vectors through the UNet to obtain the artistic character image. We demonstrate our performance by comparing the results of other models with our results. Our method reflects content information without loss and generates natural high-definition characters.

The international standard of ISO/IEC 19774 H-Anim (Humanoid Animation) defines the hierarchical structure and modeling data of a human for the generation of human animation. A common character data format is necessary in order to share and exchange characters between different applications, including gaming applications. H-Anim can be used for this purpose, and is progressing towards further standardization of new functionalities. This paper describes the modeling and animation method of a game character based on H-Anim using a general graphics tool. It also describes an algorithm to convert from H-Anim WRL, the output file generated by the general graphics tool, to H-Anim X3D based on the H-Anim specification. In addition, an experiment is described that shows compatibility of the designed H-Anim character with a general X3D browser. General behavior animations applied directly to the H-Anim character are also included.

From results of three components analysis about food waste, moisture content appeared high in order of school(89.1%), townhouse(64.9%), apartment(63.2%) and home(63.2%). And content of ash also appeared high in order of school(8.8%), townhouse(6.3%), apartment(5.4%) and home(4.2%). This is judged as cause of difference of moisture content according to emission-source, diversity of kind of cooked food and volume-rate disposal system which is not performed. Meanwhile, combustible content is 10.9~32.6% and it is the most highest in order of home, apartment, townhouse and school. And big difference of standard deviation of apartment and townhouse is judged as difference of homogeneity due to co-emission of food waste. In addition, Low heating value appeared high in order of home(1086.07 kcal/kg), apartment(1033.69 kcal/kg), townhouse (678.07 kcal/kg) and school(9.18 kcal/kg). And the reason that heating value of school is very low is error about simple formula which is applied when moisture content is more than 50%. And it can be confirmed that this is difficult in analysis of Low heating value of food waste.

게임 캐릭터의 성장은 능력 수치의 가감으로 표현되는데, 이때 능력 요소는 게임에 따라 다양하게 정의되고, 사용된다. 본 논문에서는 캐릭터의 능력 요소가 어떻게 정의되고 사용되는지 알아보고, 캐릭터의 생성할 때, 능력 수치를 부여하는 방식에 대해 제안하고자 한다. 본 방식은 캐릭터의 성장을 시간이라는 요소와 연결하여 시간의 변화에 맞추어 캐릭터가 적합한능력 수치를 가지고 생성되도록 유전 알고리즘을 활용하여 디자인한다. 시간의 변화에 맞춘실험을 통해 제안한 방식의 유효성을 검증한다.