The ability to edit 3D assets from natural language presents a compelling
paradigm to aid in the democratization of 3D content creation. However, while
natural language is often effective at communicating general intent, it is
poorly suited for specifying precise manipulation. To address this gap, we
introduce ParSEL, a system that enables controllable editing of high-quality 3D
assets from natural language. Given a segmented 3D mesh and an editing request,
ParSEL produces a parameterized editing program. Adjusting the program
parameters allows users to explore shape variations with a precise control over
the magnitudes of edits. To infer editing programs which align with an input
edit request, we leverage the abilities of large-language models (LLMs).
However, while we find that LLMs excel at identifying initial edit operations,
they often fail to infer complete editing programs, and produce outputs that
violate shape semantics. To overcome this issue, we introduce Analytical Edit
Propagation (AEP), an algorithm which extends a seed edit with additional
operations until a complete editing program has been formed. Unlike prior
methods, AEP searches for analytical editing operations compatible with a range
of possible user edits through the integration of computer algebra systems for
geometric analysis. Experimentally we demonstrate ParSEL's effectiveness in
enabling controllable editing of 3D objects through natural language requests
over alternative system designs.

通过自然语言请求，ParSEL 系统能够有效地实现对 3D 对象的可控制编辑，通过生成参数化的编辑程序，用户可以准确控制编辑操作的大小，通过整合计算机代数系统进行几何分析，AEP 算法能够搜索与各种可能用户编辑兼容的分析编辑操作，从而生成完整的编辑程序，实验证明了 ParSEL 在自然语言编辑 3D 对象方面的有效性。

ParSEL: 带有语言的参数化形状编辑

ParSEL: Parameterized Shape Editing with Language

Learning to predict reliable characteristic orientations of 3D point clouds
is an important yet challenging problem, as different point clouds of the same
class may have largely varying appearances. In this work, we introduce a novel
method to decouple the shape geometry and semantics of the input point cloud to
achieve both stability and consistency. The proposed method integrates
shape-geometry-based SO(3)-equivariant learning and shape-semantics-based
SO(3)-invariant residual learning, where a final characteristic orientation is
obtained by calibrating an SO(3)-equivariant orientation hypothesis using an
SO(3)-invariant residual rotation. In experiments, the proposed method not only
demonstrates superior stability and consistency but also exhibits
state-of-the-art performances when applied to point cloud part segmentation,
given randomly rotated inputs.

本文提出了一种新颖的方法，通过将输入的 3D 点云的形状几何与语义学分离，来实现特征取向的稳定性和一致性，该方法将形状几何基于 SO（3）等变学习和形状语义基于 SO（3）不变残差学习相结合，通过校准 SO（3）等变定向假设来获得最终的特征定向。在实验中，该方法不仅展现出卓越的稳定性和一致性，而且在给定随机旋转输入的点云部分分割时也表现出最新的性能。