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.

翻译：本文针对从给定文本描述生成可控三维纹理形状这一开放研究课题展开探索。现有方法要么需要真实标注的标题信息，要么需要大量的优化时间。为解决这些问题，我们提出了一种新型框架TAPS3D，通过伪标题训练文本引导的三维形状生成器。具体而言，基于渲染的二维图像，我们从CLIP词汇表中检索相关关键词，并利用模板构建伪标题。所构建的标题为生成的三维形状提供高层语义监督。此外，为生成精细纹理并提升几何多样性，我们提出采用低级图像正则化技术，使伪渲染图像与真实图像对齐。在推理阶段，我们的模型无需额外优化即可从给定文本直接生成三维纹理形状。通过大量实验，我们分析了每个提出的组件，并展示了该框架在生成高保真、文本相关三维纹理形状方面的有效性。