모션 데이터를 이용한 3차원 아바타 얼굴 표정 제어
Facial Expression Control of 3D Avatar using Motion Data
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참고문헌
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초록
This paper propose a method that controls facial expression of 3D avatar by having the user select a sequence of facial expressions in the space of facial expressions. And we setup its system. The space of expression is created from about 2400 frames consist of motion captured data of facial expressions. To represent the state of each expression, we use the distance matrix that represents the distances between pairs of feature points on the face. The set of distance matrices is used as the space of expressions. But this space is not such a space where one state can go to another state via the straight trajectory between them. We derive trajectories between two states from the captured set of expressions in an approximate manner. First, two states are regarded adjacent if the distance between their distance matrices is below a given threshold. Any two states are considered to have a trajectory between them if there is a sequence of adjacent states between them. It is assumed that one states goes to another state via the shortest trajectory between them. The shortest trajectories are found by dynamic programming. The space of facial expressions, as the set of distance matrices, is multidimensional. Facial expression of 3D avatar is controled in real time as the user navigates the space. To help this process, we visualized the space of expressions in 2D space by using the multidimensional scaling(MDS). To see how effective this system is, we had users control facial expressions of 3D avatar by using the system. As a result of that, users estimate that system is very useful to control facial expression of 3D avatar in real-time.
Abstract
This paper propose a method that controls facial expression of 3D avatar by having the user select a sequence of facial expressions in the space of facial expressions. And we setup its system. The space of expression is created from about 2400 frames consist of motion captured data of facial expressions. To represent the state of each expression, we use the distance matrix that represents the distances between pairs of feature points on the face. The set of distance matrices is used as the space of expressions. But this space is not such a space where one state can go to another state via the straight trajectory between them. We derive trajectories between two states from the captured set of expressions in an approximate manner. First, two states are regarded adjacent if the distance between their distance matrices is below a given threshold. Any two states are considered to have a trajectory between them if there is a sequence of adjacent states between them. It is assumed that one states goes to another state via the shortest trajectory between them. The shortest trajectories are found by dynamic programming. The space of facial expressions, as the set of distance matrices, is multidimensional. Facial expression of 3D avatar is controled in real time as the user navigates the space. To help this process, we visualized the space of expressions in 2D space by using the multidimensional scaling(MDS). To see how effective this system is, we had users control facial expressions of 3D avatar by using the system. As a result of that, users estimate that system is very useful to control facial expression of 3D avatar in real-time.
참고문헌 (16)
- Demetri Terzopoulos, "Facial animation：Past, present and future"
- Frederic I. Parke, "Computer facial animation" (1996), A. K. Peters
- Brian Guenter, "Making Faces", ACM SIGGRAPH
- Won-Sook Lee, "Model based face reconstruction for animation", pp.323~338 (1997), World Scientific Press. Singapore
- Cyriaque Kouadio, "Real-time facial animation based upon a bank of 3D facial expressions"
- Wonseok Chae, "An Example-based Approach to Text-driven Speech Animation with Emotional Expressions", 22(3) (2003)
- W. S. Torgerson, "Multidimensional scaling ：I theory and method", 17, pp.401~419 (1952)
- Young, "Multidimensional Scaling", Erlbau
- R. W. Floyd, "Algorithm 97：Shortest Path", 5, pp.345~ (1962)
- Foster, "Designing and Building Parallel Programs" (1995), Addison-Wesley
- T. Cox, "Multidimensional Scaling" (1994), Chapman & Hall
- Wolfgang Hardle,Leopold Simar, "Applied Multivariate Statistical Analysis", pp.373~392 (2003), Springer Verlag
- P. Tarazaga, "An Approximate Solution to the Metric SSTRESS Problem in Multidimensional Scaling", 30, pp.292~295 (1998)
- K. V. Mardia, "Some properties of classical multi-dimensional scaling", a7, pp.1233~1241 (1978)
- Uprendra Shardanand, "Social information filtering for music recommendation" (1994)
- Sanchez LorenzoM, "Use and Re-use of Facial Motion Capture Data", pp.135~142, University of Bath