🎬 video

MarDini

Autoregressive diffusion model for large-scale video generation

#video generation
#diffusion model
#large scale
#autoregressive
#spatial-temporal attention
MarDini

Product Details

MarDini is a video diffusion model launched by Meta AI Research that integrates the advantages of masked autoregression (MAR) into a unified diffusion model (DM) framework. The model can generate video at any frame position based on any number of mask frames, and supports a variety of video generation tasks such as video interpolation, image-to-video generation, and video expansion. MarDini is designed to be efficient, allocating most of the computing resources to low-resolution planning models, making spatial-temporal attention possible at large scales. MarDini sets a new benchmark in video interpolation and efficiently generates videos comparable to more expensive advanced image-to-video models within a few inference steps.

Main Features

1
- Video interpolation: Given the first and last frames, generate intermediate frames to achieve video interpolation.
2
- Image to video generation: Start masking from the second frame to achieve image to video generation.
3
- Video expansion: Based on the given video, expand the video through mask and add new frames.
4
- Long-term video generation: Generate long-term videos from a small number of images through recursive interpolation.
5
- 3D view synthesis: Although only trained on video data, MarDini demonstrates preliminary spatial understanding capabilities, providing possibilities for 3D applications.
6
- Flexibility: Supports a variety of video generation tasks through flexible masking strategies.
7
- Scalability: MarDini is capable of large-scale training from scratch without relying on image-based pre-training.
8
- High efficiency: Memory efficient and fast during inference, allowing large-scale deployment of computationally intensive spatial-temporal attention mechanisms.

How to Use

1
1. Visit MarDini’s product page and download the relevant models and code.
2
2. According to the documentation, set up the required video generation task, including selecting the masking strategy and video parameters.
3
3. Prepare input data, such as a single image or video sequence, and preprocess it in the required format.
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4. Use the MarDini model to reason on the input data and generate video content.
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5. Post-process the generated video as needed, such as editing, color correction, etc.
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6. Use the final video content for your desired application, such as social media publishing, in-game video, or film production.

Target Users

MarDini's target audience is video makers, animators, game developers and any professional who needs video content generation. It is suitable for them because it provides an efficient, flexible and scalable way to generate video content without complex pre-processing or post-editing.

Examples

Use MarDini to generate a coherent series of video frames from a single image for rapid production of social media video content.

In game development, MarDini is used to generate dynamic background videos to increase the realism of the game environment.

In film production, MarDini is used to generate intermediate frames for the production of slow-motion video effects.

Quick Access

Visit Website →

Categories

🎬 video
› Image generation
› video generation

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