  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.
