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Title: 3D Highlighter: Localizing Regions on 3D Shapes via Text Descriptions
We present 3D Highlighter, a technique for localizing semantic regions on a mesh using text as input. A key feature of our system is the ability to interpret “out-of-domain” localizations. Our system demonstrates the ability to reason about where to place non-obviously related concepts on an input 3D shape, such as adding clothing to a bare 3D animal model. Our method contextualizes the text description using a neural field and colors the corresponding region of the shape using a probability-weighted blend. Our neural optimization is guided by a pre-trained CLIP encoder, which bypasses the need for any 3D datasets or 3D annotations. Thus, 3D Highlighter is highly flexible, general, and capable of producing localizations on a myriad of input shapes. Our code is publicly available at https://github.com/threedle/3DHighlighter.  more » « less
Award ID(s):
2304481
PAR ID:
10493936
Author(s) / Creator(s):
; ;
Publisher / Repository:
IEEE/CVF
Date Published:
Journal Name:
IEEE Conference on Computer Vision and Pattern Recognition
ISSN:
2163-6648
Page Range / eLocation ID:
20930-20939
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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