frame-interpolation
Maintainer: google-research
259
| Property | Value |
|---|---|
| Run this model | Run on Replicate |
| API spec | View on Replicate |
| Github link | View on Github |
| Paper link | View on Arxiv |
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Model overview
The frame-interpolation model, developed by the Google Research team, is a high-quality frame interpolation neural network that can transform near-duplicate photos into slow-motion footage. It uses a unified single-network approach without relying on additional pre-trained networks like optical flow or depth estimation, yet achieves state-of-the-art results. The model is trainable from frame triplets alone and uses a multi-scale feature extractor with shared convolution weights across scales.
The frame-interpolation model is similar to the FILM: Frame Interpolation for Large Motion model, which also focuses on frame interpolation for large scene motion. Other related models include stable-diffusion, a latent text-to-image diffusion model, video-to-frames and frames-to-video, which split a video into frames and convert frames to a video, respectively, and lcm-animation, a fast animation model using a latent consistency model.
Model inputs and outputs
The frame-interpolation model takes two input frames and the number of times to interpolate between them. The output is a URI pointing to the interpolated frames, including the input frames, with the number of output frames determined by the "Times To Interpolate" parameter.
Inputs
- Frame1: The first input frame
- Frame2: The second input frame
- Times To Interpolate: Controls the number of times the frame interpolator is invoked. When set to 1, the output will be the sub-frame at t=0.5; when set to > 1, the output will be an interpolation video with (2^times_to_interpolate + 1) frames, at 30 fps.
Outputs
- Output: A URI pointing to the interpolated frames, including the input frames.
Capabilities
The frame-interpolation model can transform near-duplicate photos into slow-motion footage that looks as if it was shot with a video camera. It is capable of handling large scene motion and achieving state-of-the-art results without relying on additional pre-trained networks.
What can I use it for?
The frame-interpolation model can be used to create high-quality slow-motion videos from a set of near-duplicate photos. This can be particularly useful for capturing dynamic scenes or events where a video camera was not available. The model's ability to handle large scene motion makes it well-suited for a variety of applications, such as creating cinematic-quality videos, enhancing surveillance footage, or generating visual effects for film and video production.
Things to try
With the frame-interpolation model, you can experiment with different levels of interpolation by adjusting the "Times To Interpolate" parameter. This allows you to control the number of in-between frames generated, enabling you to create slow-motion footage with varying degrees of smoothness and detail. Additionally, you can try the model on a variety of input image pairs to see how it handles different types of motion and scene complexity.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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