AI-driven content generation has made remarkable progress in recent years. However, neural networks and human designers operate in fundamentally different ways, making collaboration between them challenging. We address this gap for Scalable Vector Graphics (SVG) by equipping neural networks with tools commonly used by designers, such as axis alignment and explicit continuity control at command junctions. We introduce DesigNet, a hierarchical Transformer-VAE that operates directly on SVG sequences with a continuous command parameterization. Our main contributions are two differentiable modules: a continuity self-refinement module that predicts $C^0$, $G^1$, and $C^1$ continuity for each curve point and enforces it by modifying Bézier control points, and an alignment self-refinement module with snapping capabilities for horizontal or vertical lines. DesigNet produces editable outlines and achieves competitive results against state-of-the-art methods, with notably higher accuracy in continuity and alignment. These properties ensure the outputs are easier to refine and integrate into professional design workflows. Source Code: https://github.com/TomasGuija/DesigNet.

翻译：近年来，AI驱动的内容生成取得了显著进展。然而，神经网络与人类设计师的运作方式存在根本差异，使得两者的协作充满挑战。我们通过为神经网络配备设计师常用的工具（如轴线对齐和命令连接处的显式连续性控制）来解决这一可缩放矢量图形（SVG）领域的差距。我们引入DesigNet，一种直接在连续命令参数化的SVG序列上运行的层级Transformer-VAE架构。我们的主要贡献在于两个可微分模块：一个连续性自优化模块，用于为每个曲线点预测$C^0$、$G^1$和$C^1$连续性，并通过修改贝塞尔控制点来强制执行该连续性；另一个是具有捕捉功能的对齐自优化模块，用于处理水平或垂直线条。DesigNet能够生成可编辑的轮廓，并在与最先进方法的竞争中取得具有竞争力的结果，特别是在连续性和对齐精度方面表现更为出色。这些特性确保了输出结果更容易进行细化，并能够集成到专业设计工作流程中。源代码：https://github.com/TomasGuija/DesigNet。