Spatial and Temporal Linearities in Posed and Spontaneous Smiles

Author:

Trutoiu Laura C.1,Carter Elizabeth J.1,Pollard Nancy1,Cohn Jeffrey F.2,Hodgins Jessica K.1

Affiliation:

1. Carnegie Mellon University, Pittsburgh, PA

2. University of Pittsburgh, Pittsburgh, PA

Abstract

Creating facial animations that convey an animator’s intent is a difficult task because animation techniques are necessarily an approximation of the subtle motion of the face. Some animation techniques may result in linearization of the motion of vertices in space (blendshapes, for example), and other, simpler techniques may result in linearization of the motion in time. In this article, we consider the problem of animating smiles and explore how these simplifications in space and time affect the perceived genuineness of smiles. We create realistic animations of spontaneous and posed smiles from high-resolution motion capture data for two computer-generated characters. The motion capture data is processed to linearize the spatial or temporal properties of the original animation. Through perceptual experiments, we evaluate the genuineness of the resulting smiles. Both space and time impact the perceived genuineness. We also investigate the effect of head motion in the perception of smiles and show similar results for the impact of linearization on animations with and without head motion. Our results indicate that spontaneous smiles are more heavily affected by linearizing the spatial and temporal properties than posed smiles. Moreover, the spontaneous smiles were more affected by temporal linearization than spatial linearization. Our results are in accordance with previous research on linearities in facial animation and allow us to conclude that a model of smiles must include a nonlinear model of velocities.

Funder

National Science Foundation

NSF and Disney Research, Pittsburgh

Publisher

Association for Computing Machinery (ACM)

Subject

Experimental and Cognitive Psychology,General Computer Science,Theoretical Computer Science

Reference21 articles.

Cited by 8 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Ecological Validity and the Evaluation of Avatar Facial Animation Noise;2024 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW);2024-03-16

2. The McNorm library: creating and validating a new library of emotionally expressive whole body dance movements;Psychological Research;2022-04-06

3. Dynamics of facial actions for assessing smile genuineness;PLOS ONE;2021-01-05

4. Relaxed Spatio-Temporal Deep Feature Aggregation for Real-Fake Expression Prediction;2017 IEEE International Conference on Computer Vision Workshops (ICCVW);2017-10

5. Dynamic properties of successful smiles;PLOS ONE;2017-06-28

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