DeepSVG Project Overview
DeepSVG is an exciting venture that aims to revolutionize the world of vector graphics animation through the use of deep learning technology. Sponsored by LingoSub and ThumbnailsPro, this project introduces a powerful hierarchical generative network specifically tailored for vector graphics.
Key Components and Offerings
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Hierarchical Generative Network: The core of the DeepSVG project is its innovative hierarchical generative network. This component is designed to enable the creation and animation of vector graphics in a structured and efficient manner.
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Deep Learning with SVG Data: DeepSVG includes a comprehensive library that facilitates deep learning with SVG (Scalable Vector Graphics) data. This library allows users to perform a variety of operations such as SVG file parsing, path simplification, data augmentation, and conversion of SVGs into PyTorch tensor format.
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SVG-Icons8 Dataset: The project provides access to a robust dataset named SVG-Icons8, which features 100,000 pre-processed icons. This dataset is readily available in PyTorch tensor format to aid researchers and developers in their work.
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Graphical User Interface (GUI): For ease of use, DeepSVG offers an experimental graphical user interface. This GUI is similar in style to the popular design tool Figma and supports features like keyframe creation and animation playback. It simplifies the visualization and manipulation of vector graphics.
Recent Updates
- As of December 2020, a raw SVG data loader has been added to enhance data processing capabilities.
- In September 2020, the project was recognized by being accepted at NeurIPS2020, a prestigious conference in the field of artificial intelligence.
Installation and Setup
To get started with DeepSVG, users need to clone the GitHub repository and set up a Python 3.7 environment using Conda. Installation includes downloading necessary dependencies, and there are specific instructions to ensure compatibility on various operating systems like Ubuntu and macOS.
Dataset Accessibility
The SVG-Icons8 dataset can be downloaded directly using provided scripts or manually via Google Drive. It is structured to enable easy access and use. Additionally, an alternative Font dataset is available for download, with instructions provided for users who want to explore font generation and manipulation.
SVG Library for Deep Learning
DeepSVG’s SVG library supports several advanced features, including:
- Conversion and simplification of SVG paths.
- Data augmentation capabilities to modify graphics through operations like translation, scaling, and rotation.
The library is highly flexible, as demonstrated in an example where a dolphin SVG is visually transformed and animated easily.
Differentiable SVG Shape Optimization
DeepSVG offers functionality for differentiable SVG shape optimization, similar to techniques used in PyTorch3D. This feature supports gradient descent optimization to achieve artistic interpretations of target shapes.
Graphical User Interface (GUI)
While still experimental, the GUI built for DeepSVG allows for intuitive interaction with vector animations. Despite being in the demo phase, the GUI supports essential functions for creating basic 2D animations and more features are expected to be added soon.
Training and Inference
The project provides well-structured commands and scripts to facilitate training of the model using configurable parameters. Pretrained models are available for download to allow immediate experimentation with interpolation and other functionalities.
Contribution and License
DeepSVG is open to contributions from the community. Interested developers can submit pull requests for bug fixes or new features. The project is released under the MIT license, encouraging open collaboration and innovation.
Conclusion
DeepSVG represents a significant advancement in the field of vector graphics animation, combining deep learning with intricate SVG data manipulation. By providing robust tools, datasets, and a user-friendly interface, it empowers both researchers and developers to push the boundaries of what's possible in digital graphic design and animation.