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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. 

Achieving expressive 3D motion reconstruction and automatic generation for isolated sign words can be challenging, due to the lack of real-world 3D sign-word data, the complex nuances of signing motions, and the cross-modal understanding of sign language semantics. To address these challenges, we introduce SignAvatar, a framework capable of both word-level sign language reconstruction and generation. SignAvatar employs a transformer-based conditional variational autoencoder architecture, effectively establishing relationships across different semantic modalities. Additionally, this approach incorporates a curriculum learning strategy to enhance the model's robustness and generalization, resulting in more realistic motions. Furthermore, we contribute the ASL3DWord dataset, composed of 3D joint rotation data for the body, hands, and face, for unique sign words. We demonstrate the effectiveness of SignAvatar through extensive experiments, showcasing its superior reconstruction and automatic generation capabilities. The code and dataset are available on the project page