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Mardini
Overview :
MarDini is a video diffusion model launched by Meta AI Research, integrating the advantages of Masked Auto-Regressive (MAR) within a unified Diffusion Model (DM) framework. This model enables video generation at any frame position based on any number of masked frames, supporting various video generation tasks such as video interpolation, image-to-video generation, and video extension. MarDini is designed to allocate most computational resources to a low-resolution planning model, making large-scale space-time attention feasible. MarDini sets new benchmarks in video interpolation and efficiently generates videos comparable to more costly advanced image-to-video models within a few inference steps.
Target Users :
MarDini is designed for video creators, animators, game developers, and any professionals who require video content generation. It suits their needs as it provides an efficient, flexible, and scalable method for generating video content without the need for complex preprocessing or post-editing.
Use Cases
Use MarDini to generate a series of coherent video frames from a single image for rapid production of social media video content.
In game development, utilize MarDini to create dynamic background videos, enhancing the realism of the game environment.
In film production, generate intermediate frames with MarDini for creating slow-motion video effects.
Features
- Video Interpolation: Generate intermediate frames between given start and end frames for video interpolation.
- Image to Video Generation: Create video from the second frame by masking.
- Video Extension: Expand a video by adding new frames based on a given video through masking.
- Long-duration Video Generation: Create long-duration videos from a few images using recursive interpolation.
- 3D View Synthesis: MarDini demonstrates preliminary spatial understanding capabilities, opening up possibilities for 3D applications, despite being trained only on video data.
- Flexibility: Supports various video generation tasks through flexible masking strategies.
- Scalability: Capable of large-scale training from scratch without relying on image-based pre-training.
- High Efficiency: Offers high memory efficiency and fast speed during inference, allowing for large-scale deployment of computationally intensive space-time attention mechanisms.
How to Use
1. Visit the MarDini product page and download the relevant models and code.
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2. Set up the desired video generation tasks as per the documentation, including choosing masking strategies and video parameters.
3. Prepare the input data, such as a single image or a sequence of videos, and preprocess it according to the required format.
4. Use the MarDini model to infer from the input data and generate the video content.
5. Perform any necessary post-processing on the generated videos, such as editing and color correction.
6. Utilize the final video content across desired applications, such as social media publishing, in-game videos, or film production.
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