- The paper introduces a novel GAN inversion technique that generates diverse and semantically coherent image outpaintings.
- It employs a StyleGAN2-based generator with spatial and categorical conditioning to effectively expand and enrich image content.
- Experimental results demonstrate significant improvements in FID, IS, and LPIPS metrics, validating superior quality over state-of-the-art methods.
Diverse Image Outpainting via GAN Inversion
The paper "InOut: Diverse Image Outpainting via GAN Inversion" presents a novel methodology for tackling the intricate task of image outpainting. Employing Generative Adversarial Networks (GANs) and leveraging GAN inversion techniques, the authors offer an innovative approach toward synthesizing semantically consistent extensions of input images. The primary focus rests on expanding image regions beyond their existing boundaries in a manner that promises visual richness and diversity, a non-trivial problem given the limited boundary constraints associated with outpainting tasks.
Main Contributions
The authors distinguish their approach by utilizing a GAN inversion technique to tackle image outpainting—a task typically approached using image-to-image translation (I2I) paradigms. In contrast to conventional methods that often produce repetitive structures by leveraging strong conditioning from input images, the proposed method leverages GAN inversion to navigate the latent space more effectively. This allows for the generation of diverse outpainting solutions characterized by unique content and structure.
The generator orchestrated using a StyleGAN2-based architecture optimizes micro-patch generation, which is conditioned both on the latent code and the spatial coordinates within an image. This facilitates the strong capability to generate semantically coherent expansions beyond existing image content. A notable feature of the proposed method is the introduction of categorical conditioning, enabling user-driven manipulation of outpainted regions based on desired semantic inputs.
Experimental Validation
Extensive experiments across datasets such as Places365 and the custom-collected Flickr-Scenery dataset underscore the method's proficiency in generating high-quality and diverse outpainted images. The research articulates both qualitative and quantitative comparisons with state-of-the-art baselines, highlighting significant improvements in the visual quality and diversity of generated outputs.
The utilization of metrics such as Fréchet Inception Distance (FID) and Inception Score (IS) substantiate the method's ability to produce images that align faithfully to real-world standards in terms of realism. The Learned Perceptual Image Patch Similarity (LPIPS) metric further validates the diversity within generated outpainted regions, demonstrating the method's superior ability to produce varied yet high-fidelity extensions.
Implications and Future Directions
The implications of this work lie in its ability to alleviate prevalent issues faced by prior I2I methods in producing redundant extensions, thereby offering substantial promise for applications requiring diverse image synthesis. The categorical conditioning framework further caters to customization, enhancing the method's applicability in interactive and user-oriented scenarios.
Future research may explore improvements to GAN inversion algorithms to enhance computational efficiency and extend this approach to higher complexity scenes beyond landscapes—such as urban environments or structured interiors. As GAN architectures evolve, synergizing advancements can further enrich context-aware outpainting capabilities.
This paper establishes a robust framework advocating for GAN inversion as a powerful tool in generating diverse outpainted images, contributing a substantial advancement to the broader discourse on generative modeling and its applications.