SVGCraft: Beyond Single Object Text-to-SVG Synthesis with Comprehensive Canvas Layout
<|reference_start|>SVGCraft: Beyond Single Object Text-to-SVG Synthesis with Comprehensive Canvas Layout: Generating VectorArt from text prompts is a challenging vision task, requiring diverse yet realistic depictions of the seen as well as unseen entities. However, existing research has been mostly limited to the generation of single objects, rather than comprehensive scenes comprising multiple elements. In response, this work introduces SVGCraft, a novel end-to-end framework for the creation of vector graphics depicting entire scenes from textual descriptions. Utilizing a pre-trained LLM for layout generation from text prompts, this framework introduces a technique for producing masked latents in specified bounding boxes for accurate object placement. It introduces a fusion mechanism for integrating attention maps and employs a diffusion U-Net for coherent composition, speeding up the drawing process. The resulting SVG is optimized using a pre-trained encoder and LPIPS loss with opacity modulation to maximize similarity. Additionally, this work explores the potential of primitive shapes in facilitating canvas completion in constrained environments. Through both qualitative and quantitative assessments, SVGCraft is demonstrated to surpass prior works in abstraction, recognizability, and detail, as evidenced by its performance metrics (CLIP-T: 0.4563, Cosine Similarity: 0.6342, Confusion: 0.66, Aesthetic: 6.7832). The code will be available at https://github.com/ayanban011/SVGCraft.<|reference_end|>
@article{banerjee2024svgcraft:,
    title={SVGCraft: Beyond Single Object Text-to-SVG Synthesis with Comprehensive
  Canvas Layout},
    author={Ayan Banerjee, Nityanand Mathur, Josep Llad'os, Umapada Pal, Anjan
  Dutta},
    journal={arXiv preprint arXiv:2404.00412},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.00412},
    primaryClass={cs.CV cs.LG}
}