  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. See video at
https://youtu.be/zraPvRdl13Q
