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text2shape generating shapes from natural language by learning joint embeddings text2shape generating shapes from natural language by learning joint embeddings kevin chen christopher b choy manolis savva angel chang thomas funkhouser silvio savarese overview we present a method for generating colored 3d shapes from natural language to this end we first learn joint embeddings of freeform text descriptions and colored 3d shapes our model combines and extends learning by association and metric learning approaches to learn implicit cross modal connections and produces a joint representation that captures the many to many relations between language and physical properties of 3d shapes such as color and shape to evaluate our approach we collect a large dataset of natural language descriptions for physical 3d objects in the shapenet dataset with this learned joint embedding we demonstrate text to shape retrieval that outperforms baseline approaches using our embeddings with a novel conditional wasserstein gan framework we generate colored 3d shapes from text our method is the first to connect natural language text with realistic 3d objects exhibiting rich variations in color texture and shape detail links full paper pdf 14mb code github bibtex if you find our project helpful please consider citing us article chen2018text2shape title text2shape generating shapes from natural language by learning joint embeddings author chen kevin and choy christopher b and savva manolis and chang angel x and funkhouser thomas and savarese silvio journal arxiv preprint arxiv 1803 08495 year 2018 video summary dataset shapenet voxelizations shapenet chair and table categories only colored rgb voxelizations resolutions 32 64 and 128 surface hollow and solid voxelizations shapenet downloads shapenet dataset webpage text descriptions csv 11mb download solid voxelizations 32 resolution zip 1gb download solid voxelizations 64 resolution zip 1 7gb download solid voxelizations 128 resolution zip 4 2gb download surface voxelizations 32 resolution zip 562mb download surface voxelizations 64 resolution zip 1gb download surface voxelizations 128 resolution zip 3 1gb download primitives downloads primitive shape voxelizations 32 resolution zip 49mb download if any errors or artifacts in the dataset are found please report them to kevin chen cs stanford edu thank you note we use solid 32 resolution voxelizations in our work acknowledgements this material is based upon work supported by the national science foundation graduate research fellowship program under grant no dge 1147470 any opinions findings and conclusions or recommendations expressed in this material are those of the author s and do not necessarily reflect the views of the national science foundation this work is supported by google intel and with the support of the technical university of munich institute for advanced study funded by the german excellence initiative and the european union seventh framework programme under grant agreement no 291763