NÜWA: Visual Synthesis Pre-training for Neural visUal World creAtion

摘要

This paper presents a unified multimodal pre-trained model called NÃœWA that can generate new or manipulate existing visual data (i.e., image and video) for various visual synthesis tasks. To cover language, image, and video at the same time for different scenarios, a 3D transformer encoder-decoder framework is designed, which can not only deal with videos as 3D data but also adapt to texts and images as 1D and 2D data, respectively. A 3D Nearby Attention (3DNA) mechanism is also proposed to consider the nature of the visual data and reduce the computational complexity. We evaluate NÃœWA on 8 downstream tasks and 10 datasets. Compared to several strong baselines, NÃœWA achieves state-of-the-art results on text-to-image generation, text-to-video generation, video predictions, etc. It also shows surprisingly good zero-shot capabilities on text-guided image and video manipulation tasks.