Adaptive Obfuscation for Reusing RGB Datasets for Privacy-Preserving Human Pose Estimation

For decades, RGB data has been central in human-pose estimation (HPE). The growing legislative pressure now limits their use during deployment, as RGB frames reveal sensitive personal information (SPI). Recent work often designs and trains new anonymization models, at a high computational cost. We ask instead whether adaptive filtering is sufficient. We introduce an adaptive obfuscation pipeline that converts RGB datasets into privacy-aware counterparts without training new networks. For each image, we detect the largest face, estimate a re-identification risk from its relative area, and raise blur or pixelation until an InsightFace recognizer fails to match the original embedding; that intensity is then applied to the whole image. Applied to MS-COCO, the pipeline generates two new sets, COCO-BLUR and COCO-PIX. On COCO-BLUR, three off-the-shelf HPE models retain more than 87% of their baseline AP. We release our code at this link (GitHub repository) offering a sustainable way to anonymize RGB data.