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Modeling Stylized Character Expressions via Deep Learning

Part of the Lecture Notes in Computer Science book series (LNIP,volume 10112)

Abstract

We propose DeepExpr, a novel expression transfer approach from humans to multiple stylized characters. We first train two Convolutional Neural Networks to recognize the expression of humans and stylized characters independently. Then we utilize a transfer learning technique to learn the mapping from humans to characters to create a shared embedding feature space. This embedding also allows human expression-based image retrieval and character expression-based image retrieval. We use our perceptual model to retrieve character expressions corresponding to humans. We evaluate our method on a set of retrieval tasks on our collected stylized character dataset of expressions. We also show that the ranking order predicted by the proposed features is highly correlated with the ranking order provided by a facial expression expert and Mechanical Turk experiments.

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Notes

  1. 1.

    Project page: http://grail.cs.washington.edu/projects/deepexpr/.

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Acknowledgements

We would like to thank Jamie Austad for creating our stylized character database. We would also like to thank the creators of the rigs we used in our project: Mery (www.meryproject.com), Ray (CGTarian Online School), Malcolm (www.animSchool.com), Aia & Jules (www.animationmentor.com), and Bonnie (Josh Sobel Rigs).

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Correspondence to Deepali Aneja .

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Aneja, D., Colburn, A., Faigin, G., Shapiro, L., Mones, B. (2017). Modeling Stylized Character Expressions via Deep Learning. In: Lai, SH., Lepetit, V., Nishino, K., Sato, Y. (eds) Computer Vision – ACCV 2016. ACCV 2016. Lecture Notes in Computer Science(), vol 10112. Springer, Cham. https://doi.org/10.1007/978-3-319-54184-6_9

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