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Deaf signers who wish to communicate in their native language frequently share videos on the Web. However, videos cannot preserve privacy—as is often desirable for discussion of sensitive topics—since both hands and face convey critical linguistic information and therefore cannot be obscured without degrading communication. Deaf signers have expressed interest in video anonymization that would preserve linguistic content. However, attempts to develop such technology have thus far shown limited success. We are developing a new method for such anonymization, with input from ASL signers. We modify a motion-based image animation model to generate high-resolution videos with the signer identity changed, but with preservation of linguistically significant motions and facial expressions. An asymmetric encoder-decoder structured image generator is used to generate the high-resolution target frame from the low-resolution source frame based on the optical flow and confidence map. We explicitly guide the model to attain clear generation of hands and face by using bounding boxes to improve the loss computation. FID and KID scores are used for evaluation of the realism of the generated frames. This technology shows great potential for practical applications to benefit deaf signers.
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Dataset Infant Anonymization with Pose and Emotion Retention
We demonstrate a procedure for the anonymization of infant subjects in videos such that salient behavioral information is retained. This method also creates a new identity that is consistent temporally across video frames. We present an overview of this anonymization process, which involves moving through the latent space of a generative model with an infant specific latent space traversal technique. We apply the technique on videos of infants, a historically difficult source of data, and make comparisons to other state-of-the-art anonymization systems. Metrics demonstrate an improved ability to retain emotional content of videos during the anonymization process, even during extreme emotions or poses, while maintaining a consistent identity throughout.
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- Award ID(s):
- 2223507
- PAR ID:
- 10569918
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3503-9494-8
- Page Range / eLocation ID:
- 1 to 5
- Format(s):
- Medium: X
- Location:
- Istanbul, Turkiye
- Sponsoring Org:
- National Science Foundation
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Deaf signers who wish to communicate in their native language frequently share videos on the Web. However, videos cannot preserve privacy—as is often desirable for discussion of sensitive topics—since both hands and face convey critical linguistic information and therefore cannot be obscured without degrading communication. Deaf signers have expressed interest in video anonymization that would preserve linguistic content. However, attempts to develop such technology have thus far shown limited success. We are developing a new method for such anonymization, with input from ASL signers. We modify a motion-based image animation model to generate high-resolution videos with the signer identity changed, but with preservation of linguistically significant motions and facial expressions. An asymmetric encoder-decoder structured image generator is used to generate the high-resolution target frame from the low-resolution source frame based on the optical flow and confidence map. We explicitly guide the model to attain clear generation of hands and face by using bounding boxes to improve the loss computation. FID and KID scores are used for evaluation of the realism of the generated frames. This technology shows great potential for practical applications to benefit deaf signers.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/FG59268.2024.10581938
