Less Static, More Private: Towards Transferable Privacy-Preserving Action Recognition by Generative Decoupled Learning

This work focuses on the task of privacy-preserving action recognition (PPAR), which aims to protect individual privacy in action videos without compromising recognition performance. Despite recent advancements, existing PPAR models still struggle with video domain shifts. To address this challenge, this work aims to develop transferable PPAR models by leveraging labeled videos from the source domain and unlabeled videos from the target domain. This work contributes a novel method named GenPriv, which improves the transferability of privacy-preserving models by generative decoupled learning. Inspired by the fact that privacy-sensitive information in action videos primarily comes from static human appearances, our GenPriv decouples video features into static and dynamic aspects and then removes privacy-sensitive content from static action features. We propose a generative architecture, ST-VAE, complemented by Spatial Consistency and Temporal Alignment losses, to enhance decoupled learning. Experimental results on three benchmarks with diverse domain shifts demonstrate the effectiveness of our proposed GenPriv. The code is available at https://github.com/iSEE- Laboratory/GenPriv.