  In this paper, we investigate an open research task of generating
controllable 3D textured shapes from the given textual descriptions. Previous
works either require ground truth caption labeling or extensive optimization
time. To resolve these issues, we present a novel framework, TAPS3D, to train a
text-guided 3D shape generator with pseudo captions. Specifically, based on
rendered 2D images, we retrieve relevant words from the CLIP vocabulary and
construct pseudo captions using templates. Our constructed captions provide
high-level semantic supervision for generated 3D shapes. Further, in order to
produce fine-grained textures and increase geometry diversity, we propose to
adopt low-level image regularization to enable fake-rendered images to align
with the real ones. During the inference phase, our proposed model can generate
3D textured shapes from the given text without any additional optimization. We
conduct extensive experiments to analyze each of our proposed components and
show the efficacy of our framework in generating high-fidelity 3D textured and
text-relevant shapes.
