We present ShapeCrafter, a neural network for recursive text-conditioned 3D shape generation. Existing methods to generate text-conditioned 3D shapes consume an entire text prompt to generate a 3D shape in a single step. However, humans tend to describe shapes recursively-we may start with an initial description and progressively add details based on intermediate results. To capture this recursive process, we introduce a method to generate a 3D shape distribution, conditioned on an initial phrase, that gradually evolves as more phrases are added. Since existing datasets are insufficient for training this approach, we present Text2Shape++, a large dataset of 369K shape-text pairs that supports recursive shape generation. To capture local details that are often used to refine shape descriptions, we build on top of vector-quantized deep implicit functions that generate a distribution of high-quality shapes. Results show that our method can generate shapes consistent with text descriptions, and shapes evolve gradually as more phrases are added. Our method supports shape editing, extrapolation, and can enable new applications in human-machine collaboration for creative design.

翻译：我们提出ShapeCrafter，一种用于递归式文本条件三维形状生成的神经网络。现有文本条件三维形状生成方法会一次性处理整个文本提示以生成三维形状。然而，人类描述形状的方式通常是递归的——从初始描述开始，逐步根据中间结果添加细节。为捕捉这一递归过程，我们提出一种方法，能够生成基于初始短语的三维形状分布，并在添加更多短语时逐步演化。由于现有数据集不足以训练该方法，我们构建了Text2Shape++，一个包含369K个形状-文本对的大规模数据集，可支持递归形状生成。为捕捉常用于细化形状描述的局部细节，我们基于向量量化深度隐函数构建模型，以生成高质量形状分布。结果表明，我们的方法能够生成与文本描述一致的形状，且形状会随短语增加而逐步演化。该方法支持形状编辑、外推，并可为人机协作创意设计提供新的应用场景。