Towards Layer-wise Image Vectorization (2206.04655v1)

Abstract: Image rasterization is a mature technique in computer graphics, while image vectorization, the reverse path of rasterization, remains a major challenge. Recent advanced deep learning-based models achieve vectorization and semantic interpolation of vector graphs and demonstrate a better topology of generating new figures. However, deep models cannot be easily generalized to out-of-domain testing data. The generated SVGs also contain complex and redundant shapes that are not quite convenient for further editing. Specifically, the crucial layer-wise topology and fundamental semantics in images are still not well understood and thus not fully explored. In this work, we propose Layer-wise Image Vectorization, namely LIVE, to convert raster images to SVGs and simultaneously maintain its image topology. LIVE can generate compact SVG forms with layer-wise structures that are semantically consistent with human perspective. We progressively add new bezier paths and optimize these paths with the layer-wise framework, newly designed loss functions, and component-wise path initialization technique. Our experiments demonstrate that LIVE presents more plausible vectorized forms than prior works and can be generalized to new images. With the help of this newly learned topology, LIVE initiates human editable SVGs for both designers and other downstream applications. Codes are made available at https://github.com/Picsart-AI-Research/LIVE-Layerwise-Image-Vectorization.

Layer-wise Image Vectorization: A Technical Overview

The paper "Towards Layer-wise Image Vectorization" presents an innovative approach to transforming raster images into Scalable Vector Graphics (SVG) through a layer-wise methodology, termed LIVE. This approach seeks to address limitations in existing vectorization techniques, particularly the lack of effective topological understanding and generalization to out-of-domain data.

Methodology

LIVE operates by progressively learning the structure of images through the addition and optimization of paths in a layer-wise manner. Unlike previous methods reliant on deep learning models that require extensive data and suffer from limitations in generalization, LIVE is a model-free approach. It incorporates three core innovations:

1. Component-wise Path Initialization: This technique intelligently identifies prominent image components for path initialization, enhancing the accuracy and efficiency of vectorization.
2. Loss Functions: The Unsigned Distance guided Focal (UDF) loss and the Self-Crossing (Xing) loss are introduced to address optimization challenges. UDF loss focuses on contour accuracy, minimizing mean color bias inherent in MSE loss. Xing loss is designed to prevent self-intersections of paths, which can degrade the vectorization quality.
3. Layer-wise Representation: Through its recursive optimization framework, LIVE constructs vector graphics that maintain topological consistency with human understanding, simplifying further image manipulation and use in design applications.

Experimental Evaluation

The paper evaluates LIVE on two diverse datasets: the Emoji dataset, consisting of simple, topologically clear images, and the Pics dataset, which includes more complex visuals. LIVE demonstrates superior performance in both qualitative and quantitative analyses, showcasing lower mean square error rates compared to traditional vectorization methods. The proposed method achieves a more faithful representation of image structure with fewer vector paths.

Implications and Future Directions

Practically, LIVE offers efficient SVG generation suitable for designers needing editable vector formats and applications involving image understanding. Theoretically, it proposes a framework that can be expanded to integrate more sophisticated shape primitives and consider additional factors like shading and texture in vectorization.

Future research could explore enhancing LIVE’s efficiency through hybrid approaches that combine the intuitive path-based optimization with learning-based segmentation techniques. Additionally, application to more complex and natural images remains an open area for exploration, potentially integrating amodal segmentation to capture intricate details.

Conclusion

This research marks a significant step in image vectorization, shifting the focus towards a more human-analogous representation. While it resolves several existing flaws in vectorization technology, the pathway for future enhancements is wide, offering exciting opportunities for further developments in graphics processing and AI applications. The availability of the code provides an accessible means for the community to build upon these findings, inviting adaptation and iteration in varied use-cases and domains.