EditGAN: High-Precision Semantic Image Editing (2111.03186v1)

Abstract: Generative adversarial networks (GANs) have recently found applications in image editing. However, most GAN based image editing methods often require large scale datasets with semantic segmentation annotations for training, only provide high level control, or merely interpolate between different images. Here, we propose EditGAN, a novel method for high quality, high precision semantic image editing, allowing users to edit images by modifying their highly detailed part segmentation masks, e.g., drawing a new mask for the headlight of a car. EditGAN builds on a GAN framework that jointly models images and their semantic segmentations, requiring only a handful of labeled examples, making it a scalable tool for editing. Specifically, we embed an image into the GAN latent space and perform conditional latent code optimization according to the segmentation edit, which effectively also modifies the image. To amortize optimization, we find editing vectors in latent space that realize the edits. The framework allows us to learn an arbitrary number of editing vectors, which can then be directly applied on other images at interactive rates. We experimentally show that EditGAN can manipulate images with an unprecedented level of detail and freedom, while preserving full image quality.We can also easily combine multiple edits and perform plausible edits beyond EditGAN training data. We demonstrate EditGAN on a wide variety of image types and quantitatively outperform several previous editing methods on standard editing benchmark tasks.

High-Precision Semantic Image Editing with EditGAN

The paper introduces EditGAN, a novel framework dedicated to high-precision semantic image editing using Generative Adversarial Networks (GANs). EditGAN primarily addresses the inherent limitations of existing GAN-based image editing methods, which often rely on large annotated datasets, provide only coarse-level control, or are restricted to interpolating between existing images. The proposed solution emphasizes the ability to precisely edit detailed image parts, such as the headlight of a car or facial features, via semantic segmentation.

Methodology

EditGAN builds on the concept introduced by DatasetGAN and SemanticGAN, which models the joint distribution of images and their pixel-wise semantic segmentation using shared latent codes. A key innovation of EditGAN lies in embedding images into the GAN's latent space, where high-precision edits can be performed by modifying segmentation masks and optimizing the latent codes accordingly. The process involves embedding images into the GAN's latent space, editing segmentation masks, and optimizing to maintain consistency with these edits.

The framework further innovates by discovering "editing vectors" in latent space. These vectors represent semantic transformations and enable the application of learned edits across different images at interactive rates without the need for intensive computational resources typically associated with conditional optimization techniques.

Experimental Results

The authors validate EditGAN through extensive experiments across various image categories, including cars, birds, cats, and human faces. Performance benchmarks demonstrate EditGAN's competence in delivering high-precision edits while outperforming several existing methods in identity preservation and attribute accuracy, with significantly reduced annotation requirements.

Through user-driven segmentation adjustments, EditGAN can perform complex, large-scale edits as well as fine-detail alterations. This flexibility is demonstrated in tasks such as changing facial expressions on human portraits while preserving identity and quality, which are crucial in applications like digital media creation and content personalization.

Furthermore, EditGAN shows superior generalization capabilities by performing edits on out-of-domain data, such as historical portrait datasets, with comparable quality to in-domain images, highlighting its robust semantic understanding.

Implications and Future Directions

EditGAN represents a considerable advancement in computational image editing, providing a tool that achieves detailed, high-quality image alterations with minimal annotated data. Practically, it opens pathways to democratizing advanced photo editing and creative content generation in disciplines such as media, art, and entertainment. Theoretically, it sets a precedent for further developments in disentangling and manipulating latent spaces for detailed image synthesis.

Future work may explore improvements in the disentanglement of editing vectors and optimization workflows to manage broader editing scopes and expedite processing times. Additionally, expanding GANs' coverage of various domains and scene complexities remains an open challenge. Enhanced models could further revolutionize semantic editing capabilities, offering even more granular control over image modifications in real-time scenarios.