A Workflow for Deforming External Body Shapes with the Help of an Anatomically Correct Skeletal Model

Polanco, Alejandra; Lafon, Yoann; Beurier, Georges; Peng, Jinye; Wang, Xuguang

doi:10.1007/978-3-031-37848-5_19

Cited by 1 publication

(2 citation statements)

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“…The second is the reconstructed joint error between the reconstructed joint parameters and the labeled joint parameters, provided in Equation (13). The third is the reconstructed vertex error between the reconstructed vertex positions and the labeled vertex positions, provided in Equation (14). The fourth to sixth are the localized vertex errors, provided in Equation (15), including the reconstructed left eye vertex error, denoted as L le r , the reconstructed right eye vertex error, denoted as L re r , and the reconstructed lip vertex error, denoted as L m r .…”

Section: Structure and Loss Function For The Proposed Rigrecognetmentioning

confidence: 99%

“…As the joint is locally deformable, the animated expression is delicate; however, the pose space lacks interpretable semantic parameters. What is more, due to the lack of triangulated plane normal constraints, unsmooth phenomena [13] such as candy-wrapping and collapsing elbows [14] may appear. The third alternative is musclebased or physical rigging, which generates the facial shape by simulating the muscles and tissues [15] or controlling facial deformation through physical curves [16].…”

Section: Introductionmentioning

confidence: 99%

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Neutral Facial Rigging from Limited Spatiotemporal Meshes

Hou,

Weng,

Zhao

et al. 2024

Electronics

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Manual facial rigging is time-consuming. Traditional automatic rigging methods lack either 3D datasets or explainable semantic parameters, which makes it difficult to retarget a certain 3D expression to a new face. To address the problem, we automatically generate a large 3D dataset containing semantic parameters, joint positions, and vertex positions from a limited number of spatiotemporal meshes. We establish an expression generator based on a multilayer perceptron with vertex constraints from the semantic parameters to the joint positions and establish an expression recognizer based on a generative adversarial structure from the joint positions to the semantic parameters. To enhance the accuracy of key facial area recognition, we add local vertex constraints for the eyes and lips, which are determined by the 3D masks computed by the proposed projection-searching algorithm. We testthe generation and recognition effects on a limited number of publicly available Metahuman meshes and self-collected meshes. Compared with existing methods, our generator has the shortest generation time of 14.78 ms and the smallest vertex relative mean square error of 1.57 × 10−3, while our recognizer has the highest accuracy of 92.92%. The ablation experiment verifies that the local constraints can improve the recognition accuracy by 3.02%. Compared with other 3D mask selection methods, the recognition accuracy is improved by 1.03%. In addition, our method shows robust results for meshes of different levels of detail, and the rig has more dimensions of semantic space. The source code will be made available if this paper is accepted for publication.

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Section: Structure and Loss Function For The Proposed Rigrecognetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%