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- W4312558849 abstract "We demonstrate an effective method of augmenting speech animation data, and show comparable performance to double the quantity of real data. We investigate the effect of lateral mirroring as a means of data augmentation for 3D poses in multi-speaker, speech-to-motion modelling. Our approach uses a bi-directional LSTM to generate 3D joint positions from audio features extracted using problem-agnostic speech encoder (PASE+) [7]. We demonstrate that naive mirroring for augmentation has a detrimental effect on model performance. We show our method of providing a virtual speaker identity embedding improved performance over no augmentation and was competitive with a model trained on an equal number of samples of real data." @default.
- W4312558849 created "2023-01-05" @default.
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- W4312558849 date "2022-09-06" @default.
- W4312558849 modified "2023-10-18" @default.
- W4312558849 title "Pose augmentation" @default.
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- W4312558849 doi "https://doi.org/10.1145/3514197.3549677" @default.
- W4312558849 hasPublicationYear "2022" @default.
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