@inproceedings{
cai2024auto,
title={Auto Drag{GAN}: Editing the Generative Image Manifold in an Autoregressive Manner},
author={Pengxiang Cai and Zhiwei Liu and Guibo Zhu and Yunfang Niu and Jinqiao Wang},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=tsYPhmnDuN}
}
Pixel-level fine-grained image editing remains an open challenge. Previous works fail to achieve an ideal trade-off between control granularity and inference speed. They either fail to achieve pixel-level fine-grained control, or their inference speed requires optimization. To address this, this paper for the first time employs a regression based network to learn the  variation patterns of StyleGAN latent codes during the image dragging process. This method enables pixel-level precision in drag editing with little time cost. Users can specify handle points and target points on any GAN-generated images, and our method will move each handle point to its corresponding target point. To achieve this, we decompose the entire movement process into multiple sub-processes. Specifically, we develop a encoder-decoder based network named 'Latent Predictor' to predict the latent code motion trajectories from handle points to target points in an autoregressive manner. Moreover, to enhance the prediction stability, we introduce a component named 'Latent Regularizer', aimed at constraining the latent code motion within the distribution of natural images. Extensive experiments demonstrate that our method achieves state-of-the-art (SOTA) inference speed and image editing performance at the pixel-level granularity.
Auto DragGAN: Editing the Generative Image Manifold in an Autoregressive Manner
Pengxiang Cai Zhiwei Liu Guibo Zhu Yunfang Niu Jinqiao Wang