# ParSEL: Parameterized Shape Editing with Language

arXiv: https://arxiv.org/abs/2405.20319 · HTML: https://mlsanity.com/paper/2405.20319/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 62 references that are in the map.
- First posted on arXiv 30 May 2024.

## Builds on (most cited first, 23 of 23)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [InstructPix2Pix: Learning to Follow Image Editing Instructions](https://mlsanity.com/paper/2211.09800/) (2022)
- [DiffusionCLIP: Text-Guided Diffusion Models for Robust Image Manipulation](https://mlsanity.com/paper/2110.02711/) (2021)
- [ViperGPT: Visual Inference via Python Execution for Reasoning](https://mlsanity.com/paper/2303.08128/) (2023)
- [PartNet: A Large-scale Benchmark for Fine-grained and Hierarchical Part-level 3D Object Understanding](https://mlsanity.com/paper/1812.02713/) (2018)
- [Visual Programming: Compositional visual reasoning without training](https://mlsanity.com/paper/2211.11559/) (2022)
- [Text2Mesh: Text-Driven Neural Stylization for Meshes](https://mlsanity.com/paper/2112.03221/) (2021)
- [Holodeck: Language Guided Generation of 3D Embodied AI Environments](https://mlsanity.com/paper/2312.09067/) (2023)
- [SceneCraft: An LLM Agent for Synthesizing 3D Scene as Blender Code](https://mlsanity.com/paper/2403.01248/) (2024)
- [TextDeformer: Geometry Manipulation using Text Guidance](https://mlsanity.com/paper/2304.13348/) (2023)
- [DeepMetaHandles: Learning Deformation Meta-Handles of 3D Meshes with Biharmonic Coordinates](https://mlsanity.com/paper/2102.09105/) (2021)
- [Text-Driven Image Editing via Learnable Regions](https://mlsanity.com/paper/2311.16432/) (2023)
- [L3GO: Language Agents with Chain-of-3D-Thoughts for Generating Unconventional Objects](https://mlsanity.com/paper/2402.09052/) (2024)
- [StructEdit: Learning Structural Shape Variations](https://mlsanity.com/paper/1911.11098/) (2019)
- [ShapeCoder: Discovering Abstractions for Visual Programs from Unstructured Primitives](https://mlsanity.com/paper/2305.05661/) (2023)
- [TexSliders: Diffusion-Based Texture Editing in CLIP Space](https://mlsanity.com/paper/2405.00672/) (2024)
- [3DCoMPaT++: An improved Large-scale 3D Vision Dataset for Compositional Recognition](https://mlsanity.com/paper/2310.18511/) (2023)
- [MDP: A Generalized Framework for Text-Guided Image Editing by Manipulating the Diffusion Path](https://mlsanity.com/paper/2303.16765/) (2023)
- [Learning Continuous 3D Words for Text-to-Image Generation](https://mlsanity.com/paper/2402.08654/) (2024)
- [Differentiable 3D CAD Programs for Bidirectional Editing](https://mlsanity.com/paper/2110.01182/) (2021)
- [Aladdin: Zero-Shot Hallucination of Stylized 3D Assets from Abstract Scene Descriptions](https://mlsanity.com/paper/2306.06212/) (2023)
- [Learning to Infer Semantic Parameters for 3D Shape Editing](https://mlsanity.com/paper/2011.04755/) (2020)

## Built on by (most cited first, 6 of 6)

- [LL3M: Large Language 3D Modelers](https://mlsanity.com/paper/2508.08228/) (2025)
- [LayerPeeler: Autoregressive Peeling for Layer-wise Image Vectorization](https://mlsanity.com/paper/2505.23740/) (2025)
- [ShapeLib: Designing a library of programmatic 3D shape abstractions with Large Language Models](https://mlsanity.com/paper/2502.08884/) (2025)
- [Proc3D: Procedural 3D Generation and Parametric Editing of 3D Shapes with Large Language Models](https://mlsanity.com/paper/2601.12234/) (2026)
- [ProcFunc: Function-Oriented Abstractions for Procedural 3D Generation in Python](https://mlsanity.com/paper/2604.26943/) (2026)
- [Organize Primitives into Semantic Parts: Reinforcement Reasoning for 3D Segmentation](https://mlsanity.com/paper/2610.04602/) (2026)

## Abstract (arXiv)

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.