SketchyGAN: Towards Diverse and Realistic Sketch to Image Synthesis (1801.02753v2)

Abstract: Synthesizing realistic images from human drawn sketches is a challenging problem in computer graphics and vision. Existing approaches either need exact edge maps, or rely on retrieval of existing photographs. In this work, we propose a novel Generative Adversarial Network (GAN) approach that synthesizes plausible images from 50 categories including motorcycles, horses and couches. We demonstrate a data augmentation technique for sketches which is fully automatic, and we show that the augmented data is helpful to our task. We introduce a new network building block suitable for both the generator and discriminator which improves the information flow by injecting the input image at multiple scales. Compared to state-of-the-art image translation methods, our approach generates more realistic images and achieves significantly higher Inception Scores.

• The paper presents a novel GAN architecture that converts sketches into realistic images using the Masked Residual Unit (MRU) for dynamic, multi-scale input conditioning.
• It employs an effective data augmentation strategy that pairs sketches with Flickr-derived edge maps, enhancing training across 50 object categories.
• The study reports an Inception Score of 7.90, indicating a significant improvement in image realism compared to earlier GAN-based approaches.

SketchyGAN: Advancements in Sketch-Based Image Synthesis

The paper "SketchyGAN: Towards Diverse and Realistic Sketch to Image Synthesis" details the development and evaluation of a novel Generative Adversarial Network (GAN) architecture aimed at transforming hand-drawn sketches into realistic images. This research presents significant advancements over prior methods that either required detailed edge maps or relied on photographic retrieval for image synthesis. SketchyGAN seeks to address the challenge inherent in generating realistic imagery from simple, often imprecise sketches, a task that expands the accessibility of image creation to non-artists with minimal synthesis expertise.

The authors introduce SketchyGAN, which is trained to convert sketches from 50 categories into realistic images. Important contributions of this work include the integration of a new network building block, the Masked Residual Unit (MRU), and the implementation of a data augmentation strategy. The MRU enhances the flow of information through the network by repetitively conditioning on the input sketch, thereby improving the synthesis quality. The data augmentation involves pairing the Sketchy database with a larger dataset of edge maps extracted from Flickr photographs, enabling the network to learn from a more extensive set of training examples than would be possible with sketches alone.

SketchyGAN's architecture displays several key innovations. The MRUs are employed in both the generator and discriminator networks, allowing effective utilization of multi-scale inputs dynamically, which corresponds well with the varying complexity observed in sketches. The discriminator is leveraged not only to differentiate real from synthesized images but also to enhance the recognition of diverse object categories, thereby pushing the work into a multi-class synthesis domain.

The authors report that SketchyGAN outperformed existing methods significantly, achieving an Inception Score of 7.90, a marked improvement over other GAN-based approaches such as pix2pix. This numerical result translates to the generation of images that better approximate real-world counterparts, as rated by both quantitative evaluation and human assessment on realism and faithfulness to input sketches.

Another central element of SketchyGAN's framework is its training schedule, a novel approach that gradually transitions the network's focus from edge map-image pairs to sketch-image pairs. This transition allows the model to leverage the structural information available in edge maps before fine-tuning the generation capabilities on less detailed, human-drawn sketches.

In examining the implications of this research, SketchyGAN not only broadens the applications of GANs in creative fields but also enhances real-time design processes, enabling rapid prototyping and simulation environments where quick visualization is advantageous. Theoretical advancements through the MRU could influence the design of future GAN architectures by promoting dynamic information conditioning strategies.

Future research avenues suggested by this work include enhancing image quality to achieve photorealism at higher resolutions and refining the model to maintain greater fidelity to artist intent without compromising realism. Exploring alternatives to enhance sketch dataset variety and introducing attention mechanisms could further improve the selective focus on essential sketch features.

Overall, the insights and methodologies offered in this paper pave the way for more robust sketch-to-image translation systems, which hold promise for various technological and artistic applications, and contribute to the ongoing evolution of AI-driven image synthesis.