发表机构
EPFL（洛桑联邦理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
AnchorGen提出一种整流流框架，通过联合建模形状与草图潜变量并在推理时施加约束，实现保持设计意图的3D生成、编辑与草图合成。
AI 中文摘要
工程设计通常始于一张固定风格和比例的2D草图，但随后的3D形状优化依赖于学习到的生成先验来保持几何有效性。然而，这些先验对草图并不知情：虽然它们通过将漂移的提案修正回其训练分布来接受有效设计，但它们往往将其修正到高密度区域，忽略了指定的设计。我们引入了AnchorGen，一个在成对数据的形状和草图潜变量上无条件训练的整流流框架。学习到的流形表示形状-草图对的联合分布，因此约束草图组件将迭代限制在与目标风格一致的形状子流形上。由于训练不使用条件信号，约束在推理时施加：梯度下降优化形状潜变量以最小化可微的拖拽代理，同时通过逐词余弦惩罚约束草图潜变量保持接近目标草图。因此，单一模型支持设计保持优化、维度明确的编辑和仅草图的合成。
英文摘要
Engineering design often starts from a 2D sketch that fixes style and proportions, yet the subsequent 3D shape optimization relies on learned generative priors to keep the geometry valid. However, these priors are agnostic to the sketch: while they admit a valid design by correcting a drifted proposal back to its training distribution, they often correct it towards the high-density region, ignoring the specified design. We introduce \emph{AnchorGen}, a rectified-flow framework trained unconditionally on the concatenated shape and sketch latents of paired data. The learned manifold represents the joint distribution of shape-sketch pairs, so constraining the sketch component restricts the iterate to the sub-manifold of shapes consistent with a target style. Since training employs no conditioning signal, the constraint is imposed at inference: gradient descent optimizes the shape latent to minimize a differentiable drag surrogate, while constraining the sketch latent to remain close to the target sketch via a token-wise cosine penalty. A single model thereby supports design-preserving optimization, dimensionally explicit design edits, and sketch-only synthesis.
Comments29 pages
