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Paint2Pix: Interactive Painting based Progressive Image Synthesis and Editing (2208.08092v1)

Published 17 Aug 2022 in cs.CV, cs.AI, cs.LG, and cs.MM

Abstract: Controllable image synthesis with user scribbles is a topic of keen interest in the computer vision community. In this paper, for the first time we study the problem of photorealistic image synthesis from incomplete and primitive human paintings. In particular, we propose a novel approach paint2pix, which learns to predict (and adapt) "what a user wants to draw" from rudimentary brushstroke inputs, by learning a mapping from the manifold of incomplete human paintings to their realistic renderings. When used in conjunction with recent works in autonomous painting agents, we show that paint2pix can be used for progressive image synthesis from scratch. During this process, paint2pix allows a novice user to progressively synthesize the desired image output, while requiring just few coarse user scribbles to accurately steer the trajectory of the synthesis process. Furthermore, we find that our approach also forms a surprisingly convenient approach for real image editing, and allows the user to perform a diverse range of custom fine-grained edits through the addition of only a few well-placed brushstrokes. Supplemental video and demo are available at https://1jsingh.github.io/paint2pix

Citations (10)

Summary

  • The paper introduces a dual-encoder approach that converts rough paintings into high-fidelity images by integrating canvas structure with identity preservation.
  • It enables iterative image refinement by leveraging simple user inputs, facilitating both progressive creation and detailed editing without extensive annotations.
  • Results show lower FID scores and strong user preference compared to traditional GAN inversion methods, underscoring its effectiveness in practical creative applications.

Paint2Pix: Exploring Interactive Painting-based Progressive Image Synthesis and Editing

The paper "Paint2Pix: Interactive Painting-based Progressive Image Synthesis and Editing" proposes a methodology aimed at advancing the capabilities of photorealistic image synthesis through user interaction with rudimentary paintings. This exploration into conditional image synthesis is built on the observation that even primitive human paintings, when confined to a specific domain, such as human faces, convey substantial context about the final image. The approach, named Paint2Pix, facilitates this form of synthesis by mapping a user's incomplete paintings to realistic renderings and offers a user-friendly mechanism for both progressive image creation and real-image editing.

Core Methodology and Contributions

The primary advancement laid out in the paper is the Paint2Pix approach, which stands apart due to its novel utilization of rudimentary human input to guide photorealistic image creation. Paint2Pix effectively utilizes two key encoders within its architecture: the canvas encoder and the identity encoder. The canvas encoder is tasked with mapping incomplete human paintings to their possible realistic outcomes, while accommodating user inputs to modify synthesis trajectories their inputs imply. Concurrently, the identity encoder ensures that these modifications preserve the underlying identity characteristics of the image, maintaining semantic consistency throughout progressive creation.

The paper makes several significant contributions:

  1. User-driven Synthesis: Paint2Pix empowers users, including those without extensive artistic expertise, to influence the direction of image synthesis through coarse user inputs.
  2. Interactive Progressive Synthesis: By employing user rudiments alongside autonomous painting methods, Paint2Pix allows a novice to engage progressively with the synthesis process, iteratively refining outputs.
  3. Convenient Fine-grained Editing: Beyond creation, Paint2Pix lends itself well to image editing tasks, enabling nuanced modifications without heavy manual artistry by attributing interpretative understanding to user scribbles.

Comparative Analysis and Strengths

The efficacy of the Paint2Pix approach is quantitatively underscored by lower Fréchet Inception Distance (FID) scores, indicative of superior image quality when compared to existing GAN inversion methods such as Restyle, e4e, and pSp. Additionally, user studies demonstrate a strong preference for Paint2Pix outputs, whether in standalone image generation or fine-tuned edits, highlighting the advantageous user experience provided by its interpretative capabilities.

In comparison with segmentation and sketch-based methods, Paint2Pix requires fewer domain-specific annotations and remains adaptable without large datasets, thereby broadening its applicability. Furthermore, it circumvents limitations tied to GAN-inversion strategies, which are frequently bound by reliance on color-based optimizations that can misinterpret human intent when visual cues deviate from typical color schemes.

Implications and Future Directions

The research delineated in this paper showcases the potential for autonomous painting technologies to expand toward areas that require little to no artistic prerequisite. The outcomes suggest fertile grounds for developments in user-guided artificial intelligence, where the implications for enhanced user experience in digital art creation are considerable. Additionally, by circumventing traditional limitations inherent in usability and data requirements, Paint2Pix paves the way for more scalable and accessible creative AI tools.

Looking ahead, extending this methodology beyond fixed domains like faces and exploring broader applications across various artistic domains could further enhance its robustness and utility. Integrating more complex semantic edits, such as gender or age variation, presents another avenue for enhancing the semantic depth Paint2Pix can interpret and synthesize.

In conclusion, Paint2Pix stands as an insightful approach that could influence both theoretical advances in the field of computer vision and practical applications in creative industries. Its emphasis on leveraging rudimentary user inputs to guide high-fidelity image synthesis offers a promising step toward making advanced image editing both user-centered and accessible.