NÜWA: Visual Synthesis Pre-training for Neural visUal World creAtion (2111.12417v1)

Abstract: This paper presents a unified multimodal pre-trained model called N\"UWA 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\"UWA on 8 downstream tasks. Compared to several strong baselines, N\"UWA 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 https://github.com/microsoft/NUWA.

• The paper introduces a unified multimodal pre-training framework that integrates a 3D transformer and a novel 3DNA mechanism to enhance visual synthesis.
• The paper achieved state-of-the-art results across eight tasks, including text-to-image, text-to-video generation, and video prediction with improved FID and CLIPSIM scores.
• The novel architecture facilitates cross-modal learning, enabling efficient visual generation with applications in digital content creation, VR, and automated design.

Overview of NÜWA: Visual Synthesis Pre-training for Neural Visual World Creation

The paper introduces NÜWA, a multimodal pre-trained model designed to handle visual synthesis tasks. The model is adept at generating and manipulating visual data across images and videos. It employs a novel 3D transformer encoder-decoder framework paired with a 3D Nearby Attention (3DNA) mechanism to manage varied data modalities efficiently. The authors demonstrate impressive state-of-the-art results across eight different downstream tasks, notably surpassing existing benchmarks in text-to-image, text-to-video generation, and video prediction.

Technical Contributions

NÜWA's architecture includes several key innovations:

1. Unified 3D Transformer Framework: The model captures language, image, and video modalities, adapting to each with a shared decoder for diverse tasks.
2. 3D Nearby Attention Mechanism: This mechanism strategically reduces computational complexity while preserving the quality of generated outputs by focusing attention on localized data.
3. Multi-task Pre-training Approach: NÜWA leverages multi-task pre-training across distinct visual synthesis scenarios, yielding significant improvements in performance metrics such as FID and CLIPSIM.

Evaluation and Results

The evaluation underscores NÜWA's versatility and efficiency:

• Text-to-Image (T2I) and Text-to-Video (T2V) Generation: The model sets new benchmarks on datasets like MSCOCO and Kinetics, demonstrating enhanced capability in generating semantically rich visuals from text prompts.
• Video Prediction (V2V): NÜWA achieves top performance on the BAIR dataset, showcasing superior video frame prediction from minimal inputs.
• Semantic and Quality Metrics: For instance, NÜWA attained a FID score of 12.9 and CLIPSIM of 0.3429 on the MSCOCO dataset, outstripping prior models like CogView and DALL-E.

Implications

The implications of NÜWA are profound for both theoretical exploration and practical applications:

• Enhanced Visual Generation: By integrating language and vision modalities, NÜWA facilitates improved synthesis of visual content, crucial for domains such as digital content creation and VR environments.
• Efficient Modeling: The introduction of 3DNA offers an avenue to explore more computationally efficient models, addressing the high complexity typically involved in high-dimensional data processing.
• Cross-modal Learning: With its multitask learning capability, NÜWA can be foundational in cross-modal research, encouraging further exploration into how different data types can enrich AI models.

Future Directions

The research lays a foundation for several intriguing future directions:

• Scalability and Adaptability: Exploring more scalable frameworks could further optimize computational resources, making NÜWA applicable to even higher resolution data and longer video sequences.
• Cross-domain Generalization: Further refining NÜWA's zero-shot capabilities could improve its adaptability across varied, unseen domains and tasks, cementing its utility in real-world applications.
• Interdisciplinary Applications: NÜWA's framework might influence areas like automated design, film production, and gaming by providing tools for creators to synthesize content efficiently and imaginatively.

Overall, the introduction of NÜWA marks a significant advance in the field of neural visual synthesis, offering a robust platform for generating and manipulating multimodal visual data. The paper exemplifies the potential of integrating advanced encoder-decoder architectures and novel attention mechanisms to enhance AI capabilities across diverse visual tasks.