제한된 모션 센서와 애니메이션 데이터를 이용한 캐릭터 동작 제어
Character Motion Control by Using Limited Sensors and Animation Data
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초록
디지털 스토리텔링에 등장하는 3차원 가상 캐릭터에는 외형뿐만 아니라 자세나 동작에서도 캐릭터의 개성이 반영된 고유의 스타일이 부여된다. 그러나 사용자가 웨어러블 동작센서를 사용하여 직접 캐릭터의 신체 동작을 제어하는 경우 캐릭터 고유 의 스타일이 무시될 수 있다. 본 연구에서는 가상 캐릭터를 위해 제작된 소량의 애니메이션 데이터만을 이용하는 검색 기반 캐릭터 동작 제어 기술을 사용하여 캐릭터 고유의 스타일을 유지하는 기술을 제시한다. 대량의 학습 데이터를 필요로하는 기계학습법을 피하는 대신 소량의 애니메이션 데이터로부터 사용자의 자세와 유사한 캐릭터 자세를 직접 검색하여 사용하는 기술을 제안한다. 제시된 방법을 검증하기 위해 전문가에 의해 제작된 가상현실 게임용 캐릭터 모델과 애니메이션 데이터 를 사용하여 실험하였다. 평범한 사람의 모션캡쳐 데이터를 사용했을 때와의 결과를 비교하여 캐릭터 스타일이 보존됨을 증명하였다. 또한 동작센서의 개수를 달리한 실험을 통해 제시된 방법의 확장성을 증명하였다.
Abstract
A 3D virtual character playing a role in a digital story-telling has a unique style in its appearance and motion. Because the style reflects the unique personality of the character, it is very important to preserve the style and keep its consistency. However, when the character’s motion is directly controlled by a user’s motion who is wearing motion sensors, the unique style can be discarded. We present a novel character motion control method that uses only a small amount of animation data created only for the character to preserve the style of the character motion. Instead of machine learning approaches requiring a large amount of training data, we suggest a search-based method, which directly searches the most similar character pose from the animation data to the current user’s pose. To show the usability of our method, we conducted our experiments with a character model and its animation data created by an expert designer for a virtual reality game. To prove that our method preserves well the original motion style of the character, we compared our result with the result obtained by using general human motion capture data. In addition, to show the scalability of our method, we presented experimental results with different numbers of motion sensors.
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