joanrod/star-vector
Star Vector
Star-vector is a suite of vision-language systems designed to generate scalable vector graphics from text or image inputs. It utilizes a vision-language foundation model to treat the creation of visual elements as a structured code generation task.
The system employs a multimodal architecture that maps visual patterns and shapes to corresponding structural elements in a vector code sequence. It incorporates a render-loop feedback mechanism and reinforcement learning to iteratively refine the fidelity of the generated graphics by comparing rendered outputs against target images.
The project covers a broad range of generation and optimization capabilities, including image-to-SVG vectorization, text-to-SVG synthesis, and the production of semantically rich vector diagrams. It specifically focuses on structural shape recognition and the optimization of vector code to improve visual accuracy.
Features
- Vision-Language Models - Built upon a vision-language model architecture that integrates visual and linguistic processing to generate vector graphics.
- SVG Generators - Treats the creation of visual elements as a code generation task using a vision-language model.
- Multimodal Alignment - Uses multimodal alignment to map visual patterns and shapes from images to corresponding structural vector code.
- Visual Fidelity Optimization - Utilizes reinforcement learning to refine vector code fidelity by comparing rendered outputs against original source images.
- Error-Correction Feedback Loops - Employs a render-loop feedback mechanism to iteratively correct visual errors by comparing rendered SVG outputs against target images.
- Multimodal Models - Implements a multimodal neural network architecture to process and align images and text for graphic generation.
- Vector Code Generation - Implements a system that transforms vector graphic creation into a structured code sequence prediction task.
- Visual Render Feedback - Refines vector graphics by comparing rendered output against a target image to correct visual errors.
- Programmatic SVG Generation - Generates scalable vector graphics by treating visual design as a structured code generation task.
- Raster to Vector Converters - Converts pixel-based images into clean scalable vector graphics by recognizing shapes and structural relationships.
- Raster-to-Vector Conversions - Translates pixel-based images into scalable vector graphics by identifying shapes, layers, and structural relationships.
- Text-to-SVG Generators - Synthesizes scalable vector graphics from written descriptions using a code-centric generative approach.
- Visual Accuracy Optimization - Refines the accuracy and fidelity of vector graphics by comparing rendered output against target images.
- Diagram Synthesis - Produces clean and semantically rich vector representations of diagrams from multimodal visual or textual inputs.
- Diagram Vectorization - Produces compact and semantically rich vector representations of visual diagrams using a multimodal approach.
- Coded Graphic Fidelity - Increases the accuracy and efficiency of vector graphics using reinforcement learning and image comparison.
- Structural Shape Recognition - Identifies visual hierarchies and geometric relationships within images to produce semantically organized vector code.
