대상의 특성을 고려한 얼굴 애니메이션 전달 기술

송재원

서지반출

국문초록

얼굴 애니메이션 리타겟팅 기술은 그 효율성과 효용성으로 인해 영화 및 애니메이션 제작에 있어 나날이 각광받고 있다. 그러나 현재 기술로 사람의 얼굴 표정을 사람과 많이 다른 캐릭터(동물, 몬스터 등)에 리타겟팅 하는 데에는 한계를 보이고 있다. 또한, 대상이 되는 캐릭터의 특성에도 불구하고 항상 동일한 타이밍의 애니메이션으로 리타겟팅 된다는 단점이 있다. 따라서 본 논문에서는 캡쳐된 얼굴 애니메이션을 사람과 많이 다른 캐릭터에 성공적으로 리타겟팅 하며, 또한 각 캐릭터의 특성에 따라 애니메이션의 타이밍을 변화시키는 방법에 대하여 고찰해보고자 한다. 이를 위하여 RBF(Radial Basis Function) 와 kCCA(kernel Canonical Correlation Analysis)를 혼합한 회귀분석 모델을 제안하고, 타겟이 되는 캐릭터의 sample animation을 이용한 1D Laplacian motion warping을 다루고자 한다.

영문초록

Facial motion retargeting has been developed mainly in the direction of representing high fidelity between a source and a target model. We present a novel facial motion retargeting method that properly regards the significant characteristics of target face model. We focus on stylistic facial shapes and timings that reveal the individuality of the target model well, after the retargeting process is finished. The method works with a range of expression pairs between the source and the target facial expressions and emotional sequence pairs of the source and the target facial motions. We first construct a prediction model to place semantically corresponding facial shapes. Our hybrid retargeting model, which combines the Radial Basis Function(RBF) and Kernel Canonical Correlation Analysis(kCCA) based regression methods copes well with new input source motions without visual artifacts. 1D Laplacian motion warping follows after the shape retargeting process, replacing stylistically important emotional sequences and thus, representing the characteristics of the target face.

키워드

페이셜 애니메이션 모션 캡쳐 페이셜 리타겟팅 Facial Animation Motion Capture Facial Retargeting

참고문헌 (16건)

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참고문헌 (16건)