  In recent years, 3D models have been utilized in many applications, such as
auto-driver, 3D reconstruction, VR, and AR. However, the scarcity of 3D model
data does not meet its practical demands. Thus, generating high-quality 3D
models efficiently from textual descriptions is a promising but challenging way
to solve this problem. In this paper, inspired by the ability of human beings
to complement visual information details from ambiguous descriptions based on
their own experience, we propose a novel text-3D generation model (T2TD), which
introduces the related shapes or textual information as the prior knowledge to
improve the performance of the 3D generation model. In this process, we first
introduce the text-3D knowledge graph to save the relationship between 3D
models and textual semantic information, which can provide the related shapes
to guide the target 3D model generation. Second, we integrate an effective
causal inference model to select useful feature information from these related
shapes, which removes the unrelated shape information and only maintains
feature information that is strongly relevant to the textual description.
Meanwhile, to effectively integrate multi-modal prior knowledge into textual
information, we adopt a novel multi-layer transformer structure to
progressively fuse related shape and textual information, which can effectively
compensate for the lack of structural information in the text and enhance the
final performance of the 3D generation model. The final experimental results
demonstrate that our approach significantly improves 3D model generation
quality and outperforms the SOTA methods on the text2shape datasets.
