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.
