Scribbler: Controlling Deep Image Synthesis with Sketch and Color (1612.00835v2)

Abstract: Recently, there have been several promising methods to generate realistic imagery from deep convolutional networks. These methods sidestep the traditional computer graphics rendering pipeline and instead generate imagery at the pixel level by learning from large collections of photos (e.g. faces or bedrooms). However, these methods are of limited utility because it is difficult for a user to control what the network produces. In this paper, we propose a deep adversarial image synthesis architecture that is conditioned on sketched boundaries and sparse color strokes to generate realistic cars, bedrooms, or faces. We demonstrate a sketch based image synthesis system which allows users to 'scribble' over the sketch to indicate preferred color for objects. Our network can then generate convincing images that satisfy both the color and the sketch constraints of user. The network is feed-forward which allows users to see the effect of their edits in real time. We compare to recent work on sketch to image synthesis and show that our approach can generate more realistic, more diverse, and more controllable outputs. The architecture is also effective at user-guided colorization of grayscale images.

• The paper introduces a novel feed-forward GAN that synthesizes high-fidelity images from sketches and sparse color inputs.
• It employs an encoder-decoder with residual connections and combined content-adversarial loss to enhance user control and output realism.
• Experimental results show superior image quality and diversity compared to existing methods, opening new avenues for interactive design applications.

Controlling Deep Image Synthesis with Sketch and Color: An Examination of Scribbler

The paper "Scribbler: Controlling Deep Image Synthesis with Sketch and Color" by Sangkloy et al. presents a novel approach for controlling image generation using deep adversarial networks. The authors propose an architecture that allows users to guide the synthesis process through sketches and sparse color strokes. This methodology addresses the limitations of existing deep learning-based image generation techniques, which often render user control challenging.

Overview of the Proposed Architecture

The core of this method is a feed-forward Generative Adversarial Network (GAN) conditioned on sketched boundaries and color constraints. This structure is noteworthy for its ability to deliver real-time feedback to the user, thereby enhancing interactivity. By training on synthetic sketches and color strokes, the network learns to extrapolate sparse inputs into fully realized images across domains such as faces, cars, and bedrooms.

The paper highlights a few distinct features of the architecture:

• A combination of content and adversarial loss is utilized, enforcing both the adherence to user inputs and the production of realistic images.
• The network employs an encoder-decoder with residual connections to maintain high-resolution synthesis capabilities.
• The input consists of both sketches and sparse color strokes, allowing users granular control over the visual output without requiring extensive artistic skill.

Experimental Results and Comparisons

Comparisons with existing frameworks underscore the capabilities of Scribbler. The system surpasses models like Sketch Inversion in generating high-fidelity outputs across various image domains. The inclusion of adversarial loss results in images with more realistic textures and colors and allows for variations in output, avoiding the monotony of similar images generated by other models.

The authors provide quantitative results showcasing the diversity and control this approach offers, such as the convincing colorization of grayscale images driven by user inputs. However, they acknowledge issues such as color leakage and challenges in maintaining strict adherence to color constraints when the adversarial loss is prioritized.

Implications and Future Directions

This research contributes significantly to controllable deep image synthesis. It demonstrates that interactive, user-guided image generation is feasible with contemporary neural networks. The findings suggest a promising future for AI applications in fields requiring rapid prototyping and design, such as graphic design or virtual character creation.

While the outcomes are promising, there are limitations, such as handling rare color scenarios or scaling issues. These represent future research directions, where enhancements could involve multi-scale network architectures and better delineation of color boundaries.

The practical implications reach beyond simple artistic applications. The ability for non-experts to generate realistic images from sketches and color constraints could profoundly impact educational tools, assistive technologies, and creative industries.

In conclusion, "Scribbler" represents a significant advancement in user-controllable image synthesis. By effectively balancing user input, computational constraints, and output realism, it opens new vistas for interactive AI-driven image creation. As these methods are refined, their incorporation into broader applications will likely become increasingly prevalent.