What is Emu3
Emu3 is a native multimodal world model launched by the Beijing Academy of Artificial Intelligence. It adopts the multimodal autoregressive technical approach independently developed by BAAI and is jointly trained on images, videos, and text, enabling the model with native multimodal capabilities and achieving unified input and output of images, videos, and text. Emu3 converts various content into discrete symbols and predicts the next symbol based on a single Transformer model, simplifying the model architecture. In image generation, Emu3 can create high-quality images that meet requirements with just a text description, outperforming the specialized image generation model SDXL. In image and language understanding, Emu3 can accurately describe real-world scenes and provide appropriate text responses without relying on CLIP or pretrained language models. Emu3 can continue existing video content and naturally extend video scenes.
Main Features of Emu3
- Image Generation: Emu3 can generate high-quality images based on text descriptions and supports different resolutions and styles.
- Video Generation: Emu3 can generate videos by predicting the next symbol in a video sequence to create videos, without relying on complex video diffusion techniques.
- Video Prediction: Emu3 can naturally continue existing video content, predict what will happen next, and simulate environments, people, and animals in the physical world.
- Image-Text Understanding: Emu3 can understand the physical world and provide coherent text responses without relying on CLIP or pretrained language models.
Technical Principles of Emu3
- Next-Token Prediction: The core of Emu3 is next-token prediction, which is an autoregressive method. The model is trained to predict the next element in a sequence, whether it is text, images, or video.
- Unified Multimodal Sequence: Emu3 unifies image, text, and video data into a discrete token space, enabling a single Transformer model to process multiple types of data.
- Single Transformer Model: Emu3 uses a single Transformer model trained from scratch to process all types of data, simplifying the model architecture and improving efficiency.
- Autoregressive Generation: In generation tasks, Emu3 predicts tokens in a sequence one by one in an autoregressive manner, thereby generating images or videos.
- Image-Text Understanding: In image-text understanding tasks, Emu3 can encode images into tokens and then generate text describing the image content.
Project Addresses of Emu3
- Project Official Website：emu.baai.ac.cn/about
- GitHub Repository：https://github.com/baaivision/Emu3
- HuggingFace Model Library：https://huggingface.co/collections/BAAI/emu3-66f4e64f70850ff358a2e60f
- Technical Paper：https://baai-solution.ks3-cn-beijing.ksyuncs.com/emu3/Emu3-tech-report.pdf
Application Scenarios of Emu3
- Content Creation: Emu3 automatically generates images and videos based on text descriptions, helping artists and designers quickly realize their ideas.
- Advertising and Marketing: Generate attractive advertising materials based on Emu3 to enhance brand promotion effectiveness.
- Education: Emu3 visualizes complex concepts to enhance students' learning experience.
- Entertainment Industry: Emu3 assists in game and film production, creating realistic virtual environments.
- Design and Architecture: Emu3 is used to generate design prototypes and architectural renderings, improving design efficiency.
- E-commerce: Emu3 helps online retailers generate product display images, enhancing the shopping experience.