Vector graphics are widely used in digital art and highly favored by
designers due to their scalability and layer-wise properties. However, the
process of creating and editing vector graphics requires creativity and design
expertise, making it a time-consuming task. Recent advancements in
text-to-vector (T2V) generation have aimed to make this process more
accessible. However, existing T2V methods directly optimize control points of
vector graphics paths, often resulting in intersecting or jagged paths due to
the lack of geometry constraints. To overcome these limitations, we propose a
novel neural path representation by designing a dual-branch Variational
Autoencoder (VAE) that learns the path latent space from both sequence and
image modalities. By optimizing the combination of neural paths, we can
incorporate geometric constraints while preserving expressivity in generated
SVGs. Furthermore, we introduce a two-stage path optimization method to improve
the visual and topological quality of generated SVGs. In the first stage, a
pre-trained text-to-image diffusion model guides the initial generation of
complex vector graphics through the Variational Score Distillation (VSD)
process. In the second stage, we refine the graphics using a layer-wise image
vectorization strategy to achieve clearer elements and structure. We
demonstrate the effectiveness of our method through extensive experiments and
showcase various applications. The project page is
this https URL

通过设计双分支变分自编码器 (VAE) 的神经路径表示，我们提出了一种新颖的神经路径表示，从序列和图像模态中学习路径潜在空间，通过优化神经路径的组合，我们可以在生成的 SVG 中融入几何约束的同时保持表达力。此外，我们引入了一种两阶段路径优化方法，用于改进生成的 SVG 的视觉和拓扑质量。

神经路径表示下的文本向量生成

Text-to-Vector Generation with Neural Path Representation

In the domain of differential equation-based generative modeling,
conventional approaches often rely on single-dimensional scalar values as
interpolation coefficients during both training and inference phases. In this
work, we introduce, for the first time, a multidimensional interpolant that
extends these coefficients into multiple dimensions, leveraging the stochastic
interpolant framework. Additionally, we propose a novel path optimization
problem tailored to adaptively determine multidimensional inference
trajectories, with a predetermined differential equation solver and a fixed
number of function evaluations. Our solution involves simulation dynamics
coupled with adversarial training to optimize the inference path. Notably,
employing a multidimensional interpolant during training improves the model's
inference performance, even in the absence of path optimization. When the
adaptive, multidimensional path derived from our optimization process is
employed, it yields further performance gains, even with fixed solver
configurations. The introduction of multidimensional interpolants not only
enhances the efficacy of models but also opens up a new domain for exploration
in training and inference methodologies, emphasizing the potential of
multidimensional paths as an untapped frontier.

在微分方程生成建模领域中，我们首次引入了一种多维插值器，将这些系数扩展到多个维度上，在训练和推理阶段都使用单一维度标量值作为插值系数。此外，我们提出了一个新的路径优化问题，用于自适应确定多维推理轨迹，其中包括预定的微分方程求解器和固定数量的函数评估。我们的解决方案使用模拟动态和对抗性训练来优化推理路径。特别地，即使在没有路径优化的情况下，使用多维插值器在训练过程中也能提高模型的推理性能。当使用由我们的优化过程导出的自适应多维路径时，即使使用固定的求解器配置，也能进一步提高性能。多维插值器的引入不仅增强了模型的有效性，还开辟了在训练和推理方法论中探索的新领域，强调了多维路径作为一个未开发的前沿。

多维插值器

Multidimensional Interpolants

Understanding the multiple socially-acceptable future behaviors is an
essential task for many vision applications. In this paper, we propose a
tree-based method, termed as Social Interpretable Tree (SIT), to address this
multi-modal prediction task, where a hand-crafted tree is built depending on
the prior information of observed trajectory to model multiple future
trajectories. Specifically, a path in the tree from the root to leaf represents
an individual possible future trajectory. SIT employs a coarse-to-fine
optimization strategy, in which the tree is first built by high-order velocity
to balance the complexity and coverage of the tree and then optimized greedily
to encourage multimodality. Finally, a teacher-forcing refining operation is
used to predict the final fine trajectory. Compared with prior methods which
leverage implicit latent variables to represent possible future trajectories,
the path in the tree can explicitly explain the rough moving behaviors (e.g.,
go straight and then turn right), and thus provides better interpretability.
Despite the hand-crafted tree, the experimental results on ETH-UCY and Stanford
Drone datasets demonstrate that our method is capable of matching or exceeding
the performance of state-of-the-art methods. Interestingly, the experiments
show that the raw built tree without training outperforms many prior deep
neural network based approaches. Meanwhile, our method presents sufficient
flexibility in long-term prediction and different best-of-$K$ predictions.

本文提出了一种基于 Social Interpretable Tree (SIT) 的树状优化算法，能够显式地解释了粗略的手势移动路径，从而提高了多模态预测的可解释性，实验结果表明，和现有深度学习模型相比，该模型能够更加精确地预测未来的运动路径。