ParSEL: Parameterized Shape Editing with Language
CoRR(2024)
Abstract
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.
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