Paper Explained - NÜWA: Visual Synthesis Pre-training for Neural visUal World creAtion (Full Video Analysis)

NÜWA is a unifying architecture that can ingest text, images, and videos and brings all of them into a quantized latent representation to support a multitude of visual generation tasks, such as text-to-image, text-guided video manipulation, or sketch-to-video. This paper details how the encoders for the different modalities are constructed, and how the latent representation is transformed using their novel 3D nearby self-attention layers. Experiments are shown on 8 different visual generation tasks that the model supports.

0:00 - Intro & Outline
1:20 - Sponsor: ClearML
3:35 - Tasks & Naming
5:10 - The problem with recurrent image generation
7:35 - Creating a shared latent space w/ Vector Quantization
23:20 - Transforming the latent representation
26:25 - Recap: Self- and Cross-Attention
28:50 - 3D Nearby Self-Attention
41:20 - Pre-Training Objective
46:05 - Experimental Results
50:40 - Conclusion & Comments

Paper: [2111.12417] NÜWA: Visual Synthesis Pre-training for Neural visUal World creAtion

Sponsor: ClearML

This paper presents a unified multimodal pre-trained model called NÜWA 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 NÜWA on 8 downstream tasks. Compared to several strong baselines, NÜWA 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 this https URL.

Authors: Chenfei Wu, Jian Liang, Lei Ji, Fan Yang, Yuejian Fang, Daxin Jiang, Nan Duan