  In this work, we explore the challenging task of generating 3D shapes from
text. Beyond the existing works, we propose a new approach for text-guided 3D
shape generation, capable of producing high-fidelity shapes with colors that
match the given text description. This work has several technical
contributions. First, we decouple the shape and color predictions for learning
features in both texts and shapes, and propose the word-level spatial
transformer to correlate word features from text with spatial features from
shape. Also, we design a cyclic loss to encourage consistency between text and
shape, and introduce the shape IMLE to diversify the generated shapes. Further,
we extend the framework to enable text-guided shape manipulation. Extensive
experiments on the largest existing text-shape benchmark manifest the
superiority of this work. The code and the models are available at
https://github.com/liuzhengzhe/Towards-Implicit Text-Guided-Shape-Generation.
