Face Anonymization Made Simple
Current face anonymization techniques often depend on identity loss
calculated by face recognition models, which can be inaccurate and unreliable.
Additionally, many methods require supplementary data such as facial landmarks
and masks to guide the synthesis process. In contrast, our approach uses
diffusion models with only a reconstruction loss, eliminating the need for
facial landmarks or masks while still producing images with intricate,
fine-grained details. We validated our results on two public benchmarks through
both quantitative and qualitative evaluations. Our model achieves
state-of-the-art performance in three key areas: identity anonymization, facial
attribute preservation, and image quality. Beyond its primary function of
anonymization, our model can also perform face swapping tasks by incorporating
an additional facial image as input, demonstrating its versatility and
potential for diverse applications. Our code and models are available at
https://github.com/hanweikung/face_anon_simple .
6728d794fd85cbe3de7d6491
Han-Wei Kung
claimed_verified
2024-11-04T21:24:15.318Z
64a3eb280111d5ff6c4849fd
/avatars/3a9000393b8d200418bae5fe7d902e4d.svg
Han-Wei Kung
user
hkung
6728d794fd85cbe3de7d6492
Tuomas Varanka
6728d794fd85cbe3de7d6493
Sanjay Saha
admin_assigned
2024-11-04T21:33:27.448Z
636f4efc5af6ffb656353f4f
/avatars/0ada75b00e4045645e0cfaa31acfbc25.svg
Sanjay Saha
user
sanjaysaha
6728d794fd85cbe3de7d6494
Terence Sim
6728d794fd85cbe3de7d6495
Nicu Sebe
Face Anonymization Made Simple
Current face anonymization techniques often depend on identity loss
calculated by face recognition models, which can be inaccurate and unreliable.
Additionally, many methods require supplementary data such as facial landmarks
and masks to guide the synthesis process. In contrast, our approach uses
diffusion models with only a reconstruction loss, eliminating the need for
facial landmarks or masks while still producing images with intricate,
fine-grained details. We validated our results on two public benchmarks through
both quantitative and qualitative evaluations. Our model achieves
state-of-the-art performance in three key areas: identity anonymization, facial
attribute preservation, and image quality. Beyond its primary function of
anonymization, our model can also perform face swapping tasks by incorporating
an additional facial image as input, demonstrating its versatility and
potential for diverse applications. Our code and models are available at
https://github.com/hanweikung/face_anon_simple .
A diffusion-based face anonymization model with reconstruction loss outperforms traditional methods by preserving identity, facial attributes, and image quality without requiring facial landmarks or masks.
diffusion models reconstruction loss identity anonymization facial attribute preservation image quality