8 citations · 16 across the 4 of their papers we have counts for
6 papers
Does Image Anonymization Impact Computer Vision Training?
Håkon Hukkelås, Frank Lindseth
Image anonymization is widely adapted in practice to comply with privacy regulations in many regions. However, anonymization often degrades the quality of the data, reducing its ut…
Synthesizing Anyone, Anywhere, in Any Pose
Håkon Hukkelås, Frank Lindseth
We address the task of in-the-wild human figure synthesis, where the primary goal is to synthesize a full body given any region in any image. In-the-wild human figure synthesis has…
DeepPrivacy2: Towards Realistic Full-Body Anonymization
Håkon Hukkelås, Frank Lindseth
Generative Adversarial Networks (GANs) are widely adapted for anonymization of human figures. However, current state-of-the-art limit anonymization to the task of face anonymizatio…
Realistic Full-Body Anonymization with Surface-Guided GANs
Håkon Hukkelås, Morten Smebye, Rudolf Mester +1
Recent work on image anonymization has shown that generative adversarial networks (GANs) can generate near-photorealistic faces to anonymize individuals. However, scaling up these…
Image Inpainting with Learnable Feature Imputation
Håkon Hukkelås, Frank Lindseth, Rudolf Mester
A regular convolution layer applying a filter in the same way over known and unknown areas causes visual artifacts in the inpainted image. Several studies address this issue with f…
DeepPrivacy: A Generative Adversarial Network for Face Anonymization
Håkon Hukkelås, Rudolf Mester, Frank Lindseth
We propose a novel architecture which is able to automatically anonymize faces in images while retaining the original data distribution. We ensure total anonymization of all faces…