TextCraft: Zero-Shot Generation of High-Fidelity and Diverse Shapes from Text
arXiv (Cornell University)(2022)
摘要
Language is one of the primary means by which we describe the 3D world around us. While rapid progress has been made in text-to-2D-image synthesis, similar progress in text-to-3D-shape synthesis has been hindered by the lack of paired (text, shape) data. Moreover, extant methods for text-to-shape generation have limited shape diversity and fidelity. We introduce TextCraft, a method to address these limitations by producing high-fidelity and diverse 3D shapes without the need for (text, shape) pairs for training. TextCraft achieves this by using CLIP and using a multi-resolution approach by first generating in a low-dimensional latent space and then upscaling to a higher resolution, improving the fidelity of the generated shape. To improve shape diversity, we use a discrete latent space which is modelled using a bidirectional transformer conditioned on the interchangeable image-text embedding space induced by CLIP. Moreover, we present a novel variant of classifier-free guidance, which further improves the accuracy-diversity trade-off. Finally, we perform extensive experiments that demonstrate that TextCraft outperforms state-of-the-art baselines.
更多查看译文
关键词
diverse shapes,textcraft,generation,zero-shot,high-fidelity
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要