Swapping Autoencoder for Deep Image Manipulation (2007.00653v2)

Abstract: Deep generative models have become increasingly effective at producing realistic images from randomly sampled seeds, but using such models for controllable manipulation of existing images remains challenging. We propose the Swapping Autoencoder, a deep model designed specifically for image manipulation, rather than random sampling. The key idea is to encode an image with two independent components and enforce that any swapped combination maps to a realistic image. In particular, we encourage the components to represent structure and texture, by enforcing one component to encode co-occurrent patch statistics across different parts of an image. As our method is trained with an encoder, finding the latent codes for a new input image becomes trivial, rather than cumbersome. As a result, it can be used to manipulate real input images in various ways, including texture swapping, local and global editing, and latent code vector arithmetic. Experiments on multiple datasets show that our model produces better results and is substantially more efficient compared to recent generative models.

• The paper demonstrates a novel latent space decomposition that splits images into independent structure and texture codes for flexible editing.
• The paper employs co-occurring patch statistics via a discriminator to maintain consistent global texture patterns during manipulation.
• The paper achieves efficient, real-time image manipulation with superior perceptual similarity and competitive FID scores compared to previous methods.

Summary of "Swapping Autoencoder for Deep Image Manipulation"

The paper presents the Swapping Autoencoder, a model designed for deep image manipulation rather than random image generation. The principal innovation is in decomposing an image into two independent latent components—structure and texture—facilitating their manipulation to produce realistic new images. This separation is achieved by enforcing criteria that keep these components independent while simultaneously allowing their arbitrary combinations to remain perceptually plausible within the image domain.

Key Contributions and Methodology

1. Latent Space Decomposition: The Swapping Autoencoder splits the latent space into structure and texture encodings via an encoder-decoder architecture, trained to capture and independently manipulate these two aspects of input images. The structure code is shaped as a spatial tensor, whereas the texture code is a global feature vector, aligning with their conceptual roles in image representation.
2. Co-occurrent Patch Statistics: To ensure that texture captures consistent global patterns across the image, the model employs a patch-based discriminator that enforces co-occurrence statistics, drawing on the classical theories of visual texture perception. This mechanism helps in maintaining stylistic consistency even when textures are swapped between different images.
3. Efficient Embedding: The autoencoder design facilitates real-time image embedding, making it significantly faster than previous methods that often rely on computationally intensive optimization procedures to project images back into latent spaces of pretrained GANs.
4. Practical Image Manipulation: By manipulating either the texture or structure components, the Swapping Autoencoder supports various editing tasks, ranging from global attribute changes to fine-grained local modifications. This versatility is demonstrated through thorough experimentation across diverse datasets, including LSUN Churches, FFHQ, and newly introduced datasets of mountains and waterfalls.

Empirical Evaluation

The model's performance is validated through both quantitative metrics and human perceptual studies. For reconstruction, the model achieves superior perceptual similarity (measured by LPIPS) compared to competing methods, while maintaining real-time execution speeds. In terms of generative realism, swap-generated image hybrids were evaluated against both traditional style transfer techniques and GAN-based editing frameworks. The Swapping Autoencoder demonstrates high fooling rates in human perceptual studies and competitive Fréchet Inception Distance (FID) scores, indicating a strong balance between realism and manipulative accuracy.

Implications and Future Work

The research highlights a pivotal direction in AI-driven image editing that focuses on disentangling semantics within self-contained models, bypassing some limitations of GANs in requiring extensive supervision or specific pretraining objectives. This works positions itself as a tool not only for visual innovations but also provides a foundational framework for further exploring factorized representations in unsupervised settings.

For future developments, addressing challenges in structured texture transfer and devising automatic metrics for more nuanced evaluation of disentanglement and realism are recommended. Additionally, considering potential misuse, it's crucial to explore domains that could benefit from image provenance verification, as generated by this model.

The Swapping Autoencoder elegantly challenges the premise that unconditional generative models are essential for high-quality image manipulations, unveiling robust avenues for creative image synthesis and interaction.