论文标题
迈向隐性文本引导的3D形状生成
Towards Implicit Text-Guided 3D Shape Generation
论文作者
论文摘要
在这项工作中，我们探讨了从文本中生成3D形状的挑战性任务。除了现有作品之外，我们还提出了一种新的方法，用于文本指导的3D形状生成，能够生产具有与给定文本描述相匹配的颜色的高保真形状。这项工作有几项技术贡献。首先，我们将文本和形状的学习特征的形状和颜色预测解除，并提出单词级的空间变压器将单词特征与文本的单词特征与形状的空间特征相关联。此外，我们设计了一个环状损失，以鼓励文本和形状之间的一致性，并引入形状IMLE以使生成的形状多样化。此外，我们扩展了框架以实现文本引导的形状操作。对现有最大的文本基准的广泛实验表明了这项工作的优势。该代码和模型可在https://github.com/liuzhengzhe/towards-implicit-implicit-text-gued-gued-shape-generation中找到。
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.
