{"id":1017309,"date":"2024-03-21T10:06:30","date_gmt":"2024-03-21T17:06:30","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=1017309"},"modified":"2024-03-21T10:06:30","modified_gmt":"2024-03-21T17:06:30","slug":"nuwa-visual-synthesis-pre-training-for-neural-visual-world-creation","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/nuwa-visual-synthesis-pre-training-for-neural-visual-world-creation\/","title":{"rendered":"N\u00dcWA: Visual Synthesis Pre-training for Neural visUal World creAtion"},"content":{"rendered":"

This paper presents a unified multimodal pre-trained model called N\u00dcWA that can generate new or manipulate existing visual data (i.e., images and videos) 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 N\u00dcWA on 8 downstream tasks. Compared to several strong baselines, N\u00dcWA achieves state-of-the-art results on text-to-image generation, text-to-video generation, video prediction, etc. Furthermore, it also shows surprisingly good zero-shot capabilities on text-guided image and video manipulation tasks. Project repo is\u00a0this https URL<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"

This paper presents a unified multimodal pre-trained model called N\u00dcWA that can generate new or manipulate existing visual data (i.e., images and videos) 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 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