  Diffusion-based generative models have recently shown remarkable image and
video editing capabilities. However, local video editing, particularly removal
of small attributes like glasses, remains a challenge. Existing methods either
alter the videos excessively, generate unrealistic artifacts, or fail to
perform the requested edit consistently throughout the video. In this work, we
focus on consistent and identity-preserving removal of glasses in videos, using
it as a case study for consistent local attribute removal in videos. Due to the
lack of paired data, we adopt a weakly supervised approach and generate
synthetic imperfect data, using an adjusted pretrained diffusion model. We show
that despite data imperfection, by learning from our generated data and
leveraging the prior of pretrained diffusion models, our model is able to
perform the desired edit consistently while preserving the original video
content. Furthermore, we exemplify the generalization ability of our method to
other local video editing tasks by applying it successfully to facial
sticker-removal. Our approach demonstrates significant improvement over
existing methods, showcasing the potential of leveraging synthetic data and
strong video priors for local video editing tasks.
