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The ability to edit 3D assets with 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 exact manipulation. To address this gap, we introduce ParSEL, a system that enablescontrollableediting of high-quality 3D assets with natural language. Given a segmented 3D mesh and an editing request, ParSEL produces aparameterizedediting program. Adjusting these parameters allows users to explore shape variations with exact control over the magnitude of the edits. To infer editing programs which align with an input edit request, we leverage the abilities of large-language models (LLMs). However, we find that although LLMs excel at identifying the initial edit operations, they often fail to infer complete editing programs, resulting in 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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EditAR: A Digital Twin Authoring Environment for Creation of AR/VR and Video Instructions from a Single Demonstration
Augmented/Virtual reality and video-based media play a vital role in the digital learning revolution to train novices in spatial tasks. However, creating content for these different media requires expertise in several fields. We present EditAR, a unified authoring, and editing environment to create content for AR, VR, and video based on a single demonstration. EditAR captures the user’s interaction within an environment and creates a digital twin, enabling users without programming backgrounds to develop content. We conducted formative interviews with both subject and media experts to design the system. The prototype was developed and reviewed by experts. We also performed a user study comparing traditional video creation with 2D video creation from 3D recordings, via a 3D editor, which uses freehand interaction for in-headset editing. Users took 5 times less time to record instructions and preferred EditAR, along with giving significantly higher usability scores.
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- Award ID(s):
- 1839971
- PAR ID:
- 10396711
- Date Published:
- Journal Name:
- IEEE International Symposium on Mixed and Augmented Reality (ISMAR)
- Page Range / eLocation ID:
- 326 to 335
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ISMAR55827.2022.00048
