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
this https URL Text-Guided-Shape-Generation.

该研究探讨了从文本生成 3D 形状的任务，提出了一种新的文本引导的 3D 形状生成方法，能够在形状与颜色上产生高保真匹配文本描述的形状，技术贡献包括基于词级空间转换器和循环损失的一致性措施，并引入了形状 IMLE 进行生成的多样性。