aiqbench analysis
EditHero establishes a critical benchmark for long-horizon, part-level 3D editing by contrasting non-agentic regeneration methods with LLM/VLM agent approaches that edit meshes via code. The analysis reveals that while agentic models better preserve unedited regions and follow complex instructions, they currently suffer from significantly slower iteration times compared to traditional methods. This work highlights the trade-off between precision in iterative workflows and computational efficiency as key challenges for future 3D asset generation pipelines.
EditHero: A Benchmark for Long-Horizon Part-Level 3D Editing and Vibe Modeling
arXiv:2610.02298v1 Announce Type: cross Abstract: 3D editing methods are usually tested on a single edit, yet an asset is built through a long sequence of revisions, each of which must implement the requested change while leaving everything else unchanged. We introduce EditHero, to our knowledge the first benchmark for long-horizon, part-level 3D editing, with natural-language instructions and target images for both geometry and texture. A deterministic assembly engine produces the exact target after every edit, and every sequence is reviewed by hand. We use EditHero to compare 2 opposite approaches to 3D editing. Non-agentic methods operate top down, regenerating the object from a learned 3D representation and inferring what to keep. In contrast, LLM/VLM agents operate bottom up, editing through code that inspects the mesh and rewrites only the parts required by instructions. The non-agentic methods often miss the requested change and disturb regions that should stay fixed. Most LLMs follow instructions more closely, and all of them preserve the unedited parts better, but each of their edits takes minutes. We will release the engine and the edit sequences to support research on reliable iterative 3D editing.
Abstract:3D editing methods are usually tested on a single edit, yet an asset is built through a long sequence of revisions, each of which must implement the requested change while leaving everything else unchanged. We introduce EditHero, to our knowledge the first benchmark for long-horizon, part-level 3D editing, with natural-language instructions and target images for both geometry and texture. A deterministic assembly engine produces the exact target after every edit, and every sequence is reviewed by hand. We use EditHero to compare 2 opposite approaches to 3D editing. Non-agentic methods operate top down, regenerating the object from a learned 3D representation and inferring what to keep. In contrast, LLM/VLM agents operate bottom up, editing through code that inspects the mesh and rewrites only the parts required by instructions. The non-agentic methods often miss the requested change and disturb regions that should stay fixed. Most LLMs follow instructions more closely, and all of them preserve the unedited parts better, but each of their edits takes minutes. We will release the engine and the edit sequences to support research on reliable iterative 3D editing.
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Graphics (cs.GR)
arXiv:2610.02298 [cs.CV]
(or arXiv:2610.02298v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2610.02298 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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