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We present 3D Highlighter, a technique for localizing semantic regions on amesh using text as input. A key feature of our system is the ability tointerpret "out-of-domain" localizations. Our system demonstrates the ability toreason about where to place non-obviously related concepts on an input 3Dshape, such as adding clothing to a bare 3D animal model. Our methodcontextualizes the text description using a neural field and colors thecorresponding region of the shape using a probability-weighted blend. Ourneural optimization is guided by a pre-trained CLIP encoder, which bypasses theneed for any 3D datasets or 3D annotations. Thus, 3D Highlighter is highlyflexible, general, and capable of producing localizations on a myriad of inputshapes. Our code is publicly available athttps://github.com/threedle/3DHighlighter.

3D Highlighter: Localizing Regions on 3D Shapes via Text Descriptions