DesigNet: Neural SVG Generation for Designers

• DesigNet is a neural framework for editable SVG generation that integrates designer-level geometric constraints.
• It utilizes a hierarchical Transformer-VAE to encode and decode SVG command sequences with continuous parameterization.
• Self-refinement modules enforce continuity and alignment, bridging the gap between neural outputs and designer workflows.

Searching arXiv for the DesigNet paper and closely related SVG/font-generation work to ground the article in current literature. DesigNet is a neural framework for generating editable Scalable Vector Graphics (SVG) in a manner explicitly aligned with professional vector-design practice. Introduced in "DesigNet: Learning to Draw Vector Graphics as Designers Do" (Guija-Valiente et al., 7 Apr 2026), it combines a hierarchical Transformer-VAE with continuous SVG command parameterization and two differentiable self-refinement modules: one for continuity at command junctions and one for horizontal or vertical alignment of straight segments. Its stated objective is to reduce the mismatch between neural generation and designer workflows by producing outlines that are not only reconstructable but also easier to refine and integrate into tools such as FontForge, Glyphs 3, or Illustrator (Guija-Valiente et al., 7 Apr 2026).

1. Scope, naming, and problem setting

DesigNet addresses SVG outline generation for fonts and icons. The problem is framed around a practical observation: AI-driven content generation has progressed rapidly, but neural networks and human designers operate in fundamentally different ways, which makes collaboration difficult. In the SVG domain, DesigNet narrows that gap by equipping the model with tools commonly used by designers, specifically axis alignment and explicit continuity control at command junctions (Guija-Valiente et al., 7 Apr 2026).

A recurrent source of confusion is its name. DesigNet is distinct from "Design Strategy Network" (Raina et al., 2021), whose acronym is DSN and whose domain is sequential truss design with hybrid action spaces. DSN factorizes action selection into region prediction and feasible-action scoring for imitation learning in a truss-design Markov decision process, whereas DesigNet operates directly on SVG command sequences and introduces deterministic geometric refinements for Bézier continuity and line alignment. The similarity is nominal rather than methodological (Raina et al., 2021).

A common misconception is to treat DesigNet as a generic SVG autoencoder. Its defining characteristic is not merely latent-variable modeling of vector sequences, but the incorporation of designer-level geometric constraints into both prediction targets and differentiable refinement operators (Guija-Valiente et al., 7 Apr 2026).

2. SVG representation and continuous command parameterization

DesigNet represents each SVG command as

$$C_{ijk} = \bigl(z_{ijk},\,\mathbf{A}_{ijk}\bigr), \qquad z_{ijk}\in\{\mathtt{MoveTo},\mathtt{LineFromTo},\mathtt{CurveFromTo},\mathtt{EOS}\},$$

where the argument tensor

$$\mathbf{A}_{ijk}=(\mathbf p^1_{ijk},\dots,\mathbf p^4_{ijk})\in\mathbb R^{4\times 2}$$

provides a unified parameterization for both straight lines and cubic Béziers (Guija-Valiente et al., 7 Apr 2026). For lines or moves, only $\mathbf p^1$ and $\mathbf p^4$ are used; a mask $\mathbf M_{ijk}\in\{0,1\}^{4\times 2}$ zeros out the unused points. This representation is central because it places heterogeneous SVG primitives into a common tensorized form without discretizing coordinates.

The command embedding is defined as

$$\mathbf e_{ijk} = \mathbf E_{\mathrm{cmd}(z_{ijk})} + f_{\mathrm{arg}}\bigl(\mathbf A_{ijk}\odot \mathbf M_{ijk}\bigr) + \mathrm{PE}(k),$$

where $\mathbf E_{\mathrm{cmd}}$ is a learnable lookup table for command types, $f_{\mathrm{arg}}$ is a linear layer, and $\mathrm{PE}(k)$ is the sinusoidal positional encoding of command index $k$ (Guija-Valiente et al., 7 Apr 2026). Because $$\mathbf{A}_{ijk}=(\mathbf p^1_{ijk},\dots,\mathbf p^4_{ijk})\in\mathbb R^{4\times 2}$$0 remains continuous, the model avoids quantization artifacts. This continuous parameterization is therefore not a minor implementation choice; it is a structural alternative to tokenized SVG representations.

The paper also emphasizes relaxed representation and auxiliary rendering losses in its ablations. This suggests that DesigNet’s representation strategy is intended to preserve both geometric fidelity and downstream editability, rather than optimizing raster similarity alone (Guija-Valiente et al., 7 Apr 2026).

3. Hierarchical Transformer-VAE architecture

DesigNet uses a hierarchical Transformer-VAE with path-level and glyph-level structure. At the path level, each path

$$\mathbf{A}_{ijk}=(\mathbf p^1_{ijk},\dots,\mathbf p^4_{ijk})\in\mathbb R^{4\times 2}$$1

is processed by a Transformer encoder $$\mathbf{A}_{ijk}=(\mathbf p^1_{ijk},\dots,\mathbf p^4_{ijk})\in\mathbb R^{4\times 2}$$2, producing embeddings $$\mathbf{A}_{ijk}=(\mathbf p^1_{ijk},\dots,\mathbf p^4_{ijk})\in\mathbb R^{4\times 2}$$3. These are average-pooled as

$$\mathbf{A}_{ijk}=(\mathbf p^1_{ijk},\dots,\mathbf p^4_{ijk})\in\mathbb R^{4\times 2}$$4

At the glyph level, the path embeddings $$\mathbf{A}_{ijk}=(\mathbf p^1_{ijk},\dots,\mathbf p^4_{ijk})\in\mathbb R^{4\times 2}$$5 are augmented with path-positional encodings and passed through a second Transformer $$\mathbf{A}_{ijk}=(\mathbf p^1_{ijk},\dots,\mathbf p^4_{ijk})\in\mathbb R^{4\times 2}$$6, after which visibility-aware pooling yields a global glyph embedding $$\mathbf{A}_{ijk}=(\mathbf p^1_{ijk},\dots,\mathbf p^4_{ijk})\in\mathbb R^{4\times 2}$$7 (Guija-Valiente et al., 7 Apr 2026).

The latent space is partitioned into a global latent and path latents. Two heads predict $$\mathbf{A}_{ijk}=(\mathbf p^1_{ijk},\dots,\mathbf p^4_{ijk})\in\mathbb R^{4\times 2}$$8 for the global posterior, and each path embedding similarly yields $$\mathbf{A}_{ijk}=(\mathbf p^1_{ijk},\dots,\mathbf p^4_{ijk})\in\mathbb R^{4\times 2}$$9. Sampling uses the reparameterization trick: $\mathbf p^1$0

$\mathbf p^1$1

This arrangement is intended to preserve global style consistency while retaining local path detail (Guija-Valiente et al., 7 Apr 2026).

Decoding also proceeds hierarchically. Each $\mathbf p^1$2 is fed to a Transformer decoder $\mathbf p^1$3 that cross-attends to $\mathbf p^1$4, producing refined path embeddings $\mathbf p^1$5. An MLP on $\mathbf p^1$6 predicts a visibility logit $\mathbf p^1$7. Then, for each path, fixed sinusoidal queries of length $\mathbf p^1$8 drive a command-level decoder $\mathbf p^1$9, which outputs per-step logits for command type $\mathbf p^4$0, continuous arguments $\mathbf p^4$1, continuity class, and alignment class (Guija-Valiente et al., 7 Apr 2026).

Training minimizes

$\mathbf p^4$2

The KL term is

$\mathbf p^4$3

while $\mathbf p^4$4 combines cross-entropy on command type, masked $\mathbf p^4$5 or Huber regression on continuous arguments, visibility classification, endpoint–start consistency $\mathbf p^4$6, and an auxiliary differentiable-rendering loss comparing sampled points along each segment (Guija-Valiente et al., 7 Apr 2026).

4. Continuity and alignment self-refinement

The continuity self-refinement module operates at junctions between consecutive segments $\mathbf p^4$7 and $\mathbf p^4$8. Tangents $\mathbf p^4$9 are computed from segment geometry: for a line, $\mathbf M_{ijk}\in\{0,1\}^{4\times 2}$0; for a Bézier, $\mathbf M_{ijk}\in\{0,1\}^{4\times 2}$1 and $\mathbf M_{ijk}\in\{0,1\}^{4\times 2}$2 (Guija-Valiente et al., 7 Apr 2026). The model distinguishes three continuity levels:

• $\mathbf M_{ijk}\in\{0,1\}^{4\times 2}$3: shared endpoint only.
• $\mathbf M_{ijk}\in\{0,1\}^{4\times 2}$4: collinear tangents,

$\mathbf M_{ijk}\in\{0,1\}^{4\times 2}$5

• $\mathbf M_{ijk}\in\{0,1\}^{4\times 2}$6: $\mathbf M_{ijk}\in\{0,1\}^{4\times 2}$7 plus matched tangent magnitude,

$\mathbf M_{ijk}\in\{0,1\}^{4\times 2}$8

For each junction $\mathbf M_{ijk}\in\{0,1\}^{4\times 2}$9, the decoder predicts a distribution $$\mathbf e_{ijk} = \mathbf E_{\mathrm{cmd}(z_{ijk})} + f_{\mathrm{arg}}\bigl(\mathbf A_{ijk}\odot \mathbf M_{ijk}\bigr) + \mathrm{PE}(k),$$0 over $$\mathbf e_{ijk} = \mathbf E_{\mathrm{cmd}(z_{ijk})} + f_{\mathrm{arg}}\bigl(\mathbf A_{ijk}\odot \mathbf M_{ijk}\bigr) + \mathrm{PE}(k),$$1. Supervision uses a cost-sensitive cross-entropy

$$\mathbf e_{ijk} = \mathbf E_{\mathrm{cmd}(z_{ijk})} + f_{\mathrm{arg}}\bigl(\mathbf A_{ijk}\odot \mathbf M_{ijk}\bigr) + \mathrm{PE}(k),$$2

where $$\mathbf e_{ijk} = \mathbf E_{\mathrm{cmd}(z_{ijk})} + f_{\mathrm{arg}}\bigl(\mathbf A_{ijk}\odot \mathbf M_{ijk}\bigr) + \mathrm{PE}(k),$$3 penalizes confusions $$\mathbf e_{ijk} = \mathbf E_{\mathrm{cmd}(z_{ijk})} + f_{\mathrm{arg}}\bigl(\mathbf A_{ijk}\odot \mathbf M_{ijk}\bigr) + \mathrm{PE}(k),$$4 more harshly (Guija-Valiente et al., 7 Apr 2026).

Refinement is differentiable through a straight-through strategy. In the forward pass, the model takes $$\mathbf e_{ijk} = \mathbf E_{\mathrm{cmd}(z_{ijk})} + f_{\mathrm{arg}}\bigl(\mathbf A_{ijk}\odot \mathbf M_{ijk}\bigr) + \mathrm{PE}(k),$$5 and modifies Bézier control points to satisfy the chosen continuity exactly. For two-curve junctions, with

$$\mathbf e_{ijk} = \mathbf E_{\mathrm{cmd}(z_{ijk})} + f_{\mathrm{arg}}\bigl(\mathbf A_{ijk}\odot \mathbf M_{ijk}\bigr) + \mathrm{PE}(k),$$6

enforcing $$\mathbf e_{ijk} = \mathbf E_{\mathrm{cmd}(z_{ijk})} + f_{\mathrm{arg}}\bigl(\mathbf A_{ijk}\odot \mathbf M_{ijk}\bigr) + \mathrm{PE}(k),$$7 sets

$$\mathbf e_{ijk} = \mathbf E_{\mathrm{cmd}(z_{ijk})} + f_{\mathrm{arg}}\bigl(\mathbf A_{ijk}\odot \mathbf M_{ijk}\bigr) + \mathrm{PE}(k),$$8

while enforcing $$\mathbf e_{ijk} = \mathbf E_{\mathrm{cmd}(z_{ijk})} + f_{\mathrm{arg}}\bigl(\mathbf A_{ijk}\odot \mathbf M_{ijk}\bigr) + \mathrm{PE}(k),$$9 additionally matches magnitudes via

$\mathbf E_{\mathrm{cmd}}$0

For line–curve junctions, the Bézier control is snapped to be exactly collinear with the line (Guija-Valiente et al., 7 Apr 2026).

The alignment self-refinement module applies to straight lines. For a line with endpoints $\mathbf E_{\mathrm{cmd}}$1 and $\mathbf E_{\mathrm{cmd}}$2, the decoder predicts $\mathbf E_{\mathrm{cmd}}$3 over $\mathbf E_{\mathrm{cmd}}$4 and minimizes

$\mathbf E_{\mathrm{cmd}}$5

Snapping uses midpoint coordinates $\mathbf E_{\mathrm{cmd}}$6 and $\mathbf E_{\mathrm{cmd}}$7: $\mathbf E_{\mathrm{cmd}}$8 The significance of these modules is explicit: DesigNet does not merely predict stylistic geometry; it predicts geometric intent classes and then enforces them through deterministic operators (Guija-Valiente et al., 7 Apr 2026).

5. Data, preprocessing, and optimization

The training setup spans three datasets. The Latin font dataset contains 16,165 fonts from 5,134 families, split into train/validation/test sets of 14,485/842/838 with no family overlap. Chinese fonts follow the DeepVecFont-v2 setting with up to 4 paths and 71 commands. The icon dataset follows DeepSVG, with up to 10 paths and 32 commands per path, for a maximum of 128 commands (Guija-Valiente et al., 7 Apr 2026).

Preprocessing normalizes all coordinates by Units-Per-EM and recenters them to $\mathbf E_{\mathrm{cmd}}$9. Samples are padded to fixed $f_{\mathrm{arg}}$0. For fonts, $f_{\mathrm{arg}}$1 and $f_{\mathrm{arg}}$2; for icons, $f_{\mathrm{arg}}$3 and $f_{\mathrm{arg}}$4, with a 128-command maximum (Guija-Valiente et al., 7 Apr 2026).

The default model uses 4 Transformer layers in each of $f_{\mathrm{arg}}$5, scaled to 10 layers for one-shot experiments. It uses 8 attention heads, $f_{\mathrm{arg}}$6, and $f_{\mathrm{arg}}$7. Optimization uses AdamW with initial learning rate $f_{\mathrm{arg}}$8, reduce-on-plateau scheduling, and batch size 64. The KL weight $f_{\mathrm{arg}}$9 is linearly annealed from 0 to 10 over the first 10K steps, with $\mathrm{PE}(k)$0 and $\mathrm{PE}(k)$1 (Guija-Valiente et al., 7 Apr 2026).

These details indicate that DesigNet was designed as a direct SVG-sequence model rather than as a raster intermediary. A plausible implication is that the architecture’s hierarchy and refinement operators were intended to remain compatible with fixed-path, fixed-command batching while still modeling editable vector structure (Guija-Valiente et al., 7 Apr 2026).

6. Evaluation, empirical behavior, and workflow implications

Evaluation uses raster and vector metrics together. The paper reports Intersection-over-Union (IoU) of rasterized glyphs, image-level $\mathrm{PE}(k)$2 distance of raster outputs, Reconstruction Error (RE) as Chamfer distance between sampled point clouds, continuity accuracy

$\mathrm{PE}(k)$3

and alignment accuracy

$\mathrm{PE}(k)$4

(Guija-Valiente et al., 7 Apr 2026).

On the Latin test split, the ablation sequence shows a progressive improvement from DeepSVG to the full model. DeepSVG reports IoU $\mathrm{PE}(k)$5, $\mathrm{PE}(k)$6 $\mathrm{PE}(k)$7, RE $\mathrm{PE}(k)$8, continuity accuracy $\mathrm{PE}(k)$9, and alignment accuracy $k$0. Adding continuous arguments, positional encoding, and centering yields IoU $k$1, $k$2 $k$3, RE $k$4, continuity accuracy $k$5, and alignment accuracy $k$6. Adding hierarchical latent space yields IoU $k$7, $k$8 $k$9, RE $$\mathbf{A}_{ijk}=(\mathbf p^1_{ijk},\dots,\mathbf p^4_{ijk})\in\mathbb R^{4\times 2}$$00, continuity accuracy $$\mathbf{A}_{ijk}=(\mathbf p^1_{ijk},\dots,\mathbf p^4_{ijk})\in\mathbb R^{4\times 2}$$01, and alignment accuracy $$\mathbf{A}_{ijk}=(\mathbf p^1_{ijk},\dots,\mathbf p^4_{ijk})\in\mathbb R^{4\times 2}$$02. Adding relaxed representation and auxiliary loss yields IoU $$\mathbf{A}_{ijk}=(\mathbf p^1_{ijk},\dots,\mathbf p^4_{ijk})\in\mathbb R^{4\times 2}$$03, $$\mathbf{A}_{ijk}=(\mathbf p^1_{ijk},\dots,\mathbf p^4_{ijk})\in\mathbb R^{4\times 2}$$04 $$\mathbf{A}_{ijk}=(\mathbf p^1_{ijk},\dots,\mathbf p^4_{ijk})\in\mathbb R^{4\times 2}$$05, RE $$\mathbf{A}_{ijk}=(\mathbf p^1_{ijk},\dots,\mathbf p^4_{ijk})\in\mathbb R^{4\times 2}$$06, continuity accuracy $$\mathbf{A}_{ijk}=(\mathbf p^1_{ijk},\dots,\mathbf p^4_{ijk})\in\mathbb R^{4\times 2}$$07, and alignment accuracy $$\mathbf{A}_{ijk}=(\mathbf p^1_{ijk},\dots,\mathbf p^4_{ijk})\in\mathbb R^{4\times 2}$$08. The self-refinement model at 75% confidence reports IoU $$\mathbf{A}_{ijk}=(\mathbf p^1_{ijk},\dots,\mathbf p^4_{ijk})\in\mathbb R^{4\times 2}$$09, $$\mathbf{A}_{ijk}=(\mathbf p^1_{ijk},\dots,\mathbf p^4_{ijk})\in\mathbb R^{4\times 2}$$10 $$\mathbf{A}_{ijk}=(\mathbf p^1_{ijk},\dots,\mathbf p^4_{ijk})\in\mathbb R^{4\times 2}$$11, RE $$\mathbf{A}_{ijk}=(\mathbf p^1_{ijk},\dots,\mathbf p^4_{ijk})\in\mathbb R^{4\times 2}$$12, continuity accuracy $$\mathbf{A}_{ijk}=(\mathbf p^1_{ijk},\dots,\mathbf p^4_{ijk})\in\mathbb R^{4\times 2}$$13, and alignment accuracy $$\mathbf{A}_{ijk}=(\mathbf p^1_{ijk},\dots,\mathbf p^4_{ijk})\in\mathbb R^{4\times 2}$$14 (Guija-Valiente et al., 7 Apr 2026).

In one-shot Latin generation, evaluated as cross-reconstruction of 52 letters from 8 references, DualVector reports IoU $$\mathbf{A}_{ijk}=(\mathbf p^1_{ijk},\dots,\mathbf p^4_{ijk})\in\mathbb R^{4\times 2}$$15 and $$\mathbf{A}_{ijk}=(\mathbf p^1_{ijk},\dots,\mathbf p^4_{ijk})\in\mathbb R^{4\times 2}$$16 $$\mathbf{A}_{ijk}=(\mathbf p^1_{ijk},\dots,\mathbf p^4_{ijk})\in\mathbb R^{4\times 2}$$17. DeepVecFont-v2 (1 shot) reports IoU $$\mathbf{A}_{ijk}=(\mathbf p^1_{ijk},\dots,\mathbf p^4_{ijk})\in\mathbb R^{4\times 2}$$18, $$\mathbf{A}_{ijk}=(\mathbf p^1_{ijk},\dots,\mathbf p^4_{ijk})\in\mathbb R^{4\times 2}$$19 $$\mathbf{A}_{ijk}=(\mathbf p^1_{ijk},\dots,\mathbf p^4_{ijk})\in\mathbb R^{4\times 2}$$20, RE $$\mathbf{A}_{ijk}=(\mathbf p^1_{ijk},\dots,\mathbf p^4_{ijk})\in\mathbb R^{4\times 2}$$21, continuity accuracy $$\mathbf{A}_{ijk}=(\mathbf p^1_{ijk},\dots,\mathbf p^4_{ijk})\in\mathbb R^{4\times 2}$$22, and alignment accuracy $$\mathbf{A}_{ijk}=(\mathbf p^1_{ijk},\dots,\mathbf p^4_{ijk})\in\mathbb R^{4\times 2}$$23, while its self-refinement variant reports IoU $$\mathbf{A}_{ijk}=(\mathbf p^1_{ijk},\dots,\mathbf p^4_{ijk})\in\mathbb R^{4\times 2}$$24, $$\mathbf{A}_{ijk}=(\mathbf p^1_{ijk},\dots,\mathbf p^4_{ijk})\in\mathbb R^{4\times 2}$$25 $$\mathbf{A}_{ijk}=(\mathbf p^1_{ijk},\dots,\mathbf p^4_{ijk})\in\mathbb R^{4\times 2}$$26, RE $$\mathbf{A}_{ijk}=(\mathbf p^1_{ijk},\dots,\mathbf p^4_{ijk})\in\mathbb R^{4\times 2}$$27, continuity accuracy $$\mathbf{A}_{ijk}=(\mathbf p^1_{ijk},\dots,\mathbf p^4_{ijk})\in\mathbb R^{4\times 2}$$28, and alignment accuracy $$\mathbf{A}_{ijk}=(\mathbf p^1_{ijk},\dots,\mathbf p^4_{ijk})\in\mathbb R^{4\times 2}$$29. DesigNet without self-refinement reports IoU $$\mathbf{A}_{ijk}=(\mathbf p^1_{ijk},\dots,\mathbf p^4_{ijk})\in\mathbb R^{4\times 2}$$30, $$\mathbf{A}_{ijk}=(\mathbf p^1_{ijk},\dots,\mathbf p^4_{ijk})\in\mathbb R^{4\times 2}$$31 $$\mathbf{A}_{ijk}=(\mathbf p^1_{ijk},\dots,\mathbf p^4_{ijk})\in\mathbb R^{4\times 2}$$32, RE $$\mathbf{A}_{ijk}=(\mathbf p^1_{ijk},\dots,\mathbf p^4_{ijk})\in\mathbb R^{4\times 2}$$33, continuity accuracy $$\mathbf{A}_{ijk}=(\mathbf p^1_{ijk},\dots,\mathbf p^4_{ijk})\in\mathbb R^{4\times 2}$$34, and alignment accuracy $$\mathbf{A}_{ijk}=(\mathbf p^1_{ijk},\dots,\mathbf p^4_{ijk})\in\mathbb R^{4\times 2}$$35. Full DesigNet reports IoU $$\mathbf{A}_{ijk}=(\mathbf p^1_{ijk},\dots,\mathbf p^4_{ijk})\in\mathbb R^{4\times 2}$$36, $$\mathbf{A}_{ijk}=(\mathbf p^1_{ijk},\dots,\mathbf p^4_{ijk})\in\mathbb R^{4\times 2}$$37 $$\mathbf{A}_{ijk}=(\mathbf p^1_{ijk},\dots,\mathbf p^4_{ijk})\in\mathbb R^{4\times 2}$$38, RE $$\mathbf{A}_{ijk}=(\mathbf p^1_{ijk},\dots,\mathbf p^4_{ijk})\in\mathbb R^{4\times 2}$$39, continuity accuracy $$\mathbf{A}_{ijk}=(\mathbf p^1_{ijk},\dots,\mathbf p^4_{ijk})\in\mathbb R^{4\times 2}$$40, and alignment accuracy $$\mathbf{A}_{ijk}=(\mathbf p^1_{ijk},\dots,\mathbf p^4_{ijk})\in\mathbb R^{4\times 2}$$41 (Guija-Valiente et al., 7 Apr 2026).

The qualitative results reinforce the intended use case. The paper shows before/after self-refinement examples with blue lines, green curves, pink continuity markers, and orange arrows pointing to corrected junctions. It also reports smooth latent interpolations using

$$\mathbf{A}_{ijk}=(\mathbf p^1_{ijk},\dots,\mathbf p^4_{ijk})\in\mathbb R^{4\times 2}$$42

and faithful icon outline recovery on DeepSVG icons (Guija-Valiente et al., 7 Apr 2026).

The broader significance lies in editability. The paper states that by exposing designer-level controls and enforcing them with deterministic, differentiable geometry operators, DesigNet’s outputs are immediately editable in FontForge, Glyphs 3, or Illustrator. It also states specific limitations: professional-grade reuse of identical motifs such as serifs across glyphs is not directly modeled, and absolute coordinates hinder exact motif tying. Proposed future directions are diffusion or flow-matching decoders in SVG space, explicit compositional frameworks for motif reuse especially for ideographic scripts, and user-in-the-loop interactive extension for fine manual adjustments (Guija-Valiente et al., 7 Apr 2026).

Taken together, these results position DesigNet as a vector-generation model whose central claim is not simply higher reconstruction quality, but tighter alignment between learned SVG generation and the geometric constraints that human designers routinely apply (Guija-Valiente et al., 7 Apr 2026).