TY - GEN
T1 - Facial Auto Rigging from 4D Expressions via Skinning Decomposition
AU - Zhao, Zhihe
AU - Weng, Dongdong
AU - Guo, Hanzhi
AU - Hou, Jing
AU - Zhou, Jixiang
N1 - Publisher Copyright:
© 2023 ACM.
PY - 2023/10/26
Y1 - 2023/10/26
N2 - This paper proposes a framework that utilizes skinning decomposition to automatically generate facial rigging from 4D expressions. The framework inputs a predefined rigging template and an actor's 4D facial expressions, including a neutral expression, as well as a group of arbitrary expressions. The output includes not only the linear blend skinning weights and joint positions of the actor's head mesh but also other facial components such as teeth and eyes. Compared to traditional methods, this paper applies a soft constraint to optimize joint positions and imposes a fixed sparsity distribution constraint to improve weight distribution. To further enhance rigging efficiency, this paper leverages GPU expression-parallel and CPU vertex-parallel strategies for joint transformation and weight updates, respectively. The experiments show that the proposed method generates high-fidelity facial rigging that outperforms existing solutions in terms of computational speed, joint position correctness, weight distribution correctness, or computational cost.
AB - This paper proposes a framework that utilizes skinning decomposition to automatically generate facial rigging from 4D expressions. The framework inputs a predefined rigging template and an actor's 4D facial expressions, including a neutral expression, as well as a group of arbitrary expressions. The output includes not only the linear blend skinning weights and joint positions of the actor's head mesh but also other facial components such as teeth and eyes. Compared to traditional methods, this paper applies a soft constraint to optimize joint positions and imposes a fixed sparsity distribution constraint to improve weight distribution. To further enhance rigging efficiency, this paper leverages GPU expression-parallel and CPU vertex-parallel strategies for joint transformation and weight updates, respectively. The experiments show that the proposed method generates high-fidelity facial rigging that outperforms existing solutions in terms of computational speed, joint position correctness, weight distribution correctness, or computational cost.
KW - 4d expressions
KW - auto rigging
KW - example-based method
KW - facial rigging
UR - http://www.scopus.com/inward/record.url?scp=85179558248&partnerID=8YFLogxK
U2 - 10.1145/3581783.3612934
DO - 10.1145/3581783.3612934
M3 - Conference contribution
AN - SCOPUS:85179558248
T3 - MM 2023 - Proceedings of the 31st ACM International Conference on Multimedia
SP - 6101
EP - 6109
BT - MM 2023 - Proceedings of the 31st ACM International Conference on Multimedia
PB - Association for Computing Machinery, Inc
T2 - 31st ACM International Conference on Multimedia, MM 2023
Y2 - 29 October 2023 through 3 November 2023
ER -