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We address the new problem of language-guided semantic style transfer of 3Dindoor scenes. The input is a 3D indoor scene mesh and several phrases thatdescribe the target scene. Firstly, 3D vertex coordinates are mapped to RGBresidues by a multi-layer perceptron. Secondly, colored 3D meshes aredifferentiablly rendered into 2D images, via a viewpoint sampling strategytailored for indoor scenes. Thirdly, rendered 2D images are compared tophrases, via pre-trained vision-language models. Lastly, errors areback-propagated to the multi-layer perceptron to update vertex colorscorresponding to certain semantic categories. We did large-scale qualitativeanalyses and A/B user tests, with the public ScanNet and SceneNN datasets. Wedemonstrate: (1) visually pleasing results that are potentially useful formultimedia applications. (2) rendering 3D indoor scenes from viewpointsconsistent with human priors is important. (3) incorporating semanticssignificantly improve style transfer quality. (4) an HSV regularization termleads to results that are more consistent with inputs and generally ratedbetter. Codes and user study toolbox are available athttps://github.com/AIR-DISCOVER/LASST

Language-guided Semantic Style Transfer of 3D Indoor Scenes