NeuralSVG: An Implicit Representation for Text-to-Vector Generation

TL;DR

NeuralSVG introduces an implicit neural approach inspired by NeRFs for generating layered, editable SVG vector graphics from text prompts, enabling high-quality, flexible, and controllable vector image creation.

Contribution

The paper presents NeuralSVG, a novel implicit neural representation for text-to-vector generation that emphasizes layered SVG structure and inference-time control, outperforming prior methods.

Findings

Outperforms existing methods in structured SVG generation

Enables dynamic SVG editing based on user input

Effectively encodes layered vector graphics with a small neural network

Abstract

Vector graphics are essential in design, providing artists with a versatile medium for creating resolution-independent and highly editable visual content. Recent advancements in vision-language and diffusion models have fueled interest in text-to-vector graphics generation. However, existing approaches often suffer from over-parameterized outputs or treat the layered structure - a core feature of vector graphics - as a secondary goal, diminishing their practical use. Recognizing the importance of layered SVG representations, we propose NeuralSVG, an implicit neural representation for generating vector graphics from text prompts. Inspired by Neural Radiance Fields (NeRFs), NeuralSVG encodes the entire scene into the weights of a small MLP network, optimized using Score Distillation Sampling (SDS). To encourage a layered structure in the generated SVG, we introduce a dropout-based regularization technique that strengthens the standalone meaning of each shape. We additionally demonstrate that utilizing a neural representation provides an added benefit of inference-time control, enabling users to dynamically adapt the generated SVG based on user-provided inputs, all with a single learned representation. Through extensive qualitative and quantitative evaluations, we demonstrate that NeuralSVG outperforms existing methods in generating structured and flexible SVG.