video-retalking
Maintainer: chenxwh
71
| 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 video-retalking model, created by maintainer chenxwh, is an AI system that can edit the faces of a real-world talking head video according to input audio, producing a high-quality and lip-synced output video even with a different emotion. This model builds upon previous work like VideoReTalking, Wav2Lip, and GANimation, disentangling the task into three sequential steps: face video generation with a canonical expression, audio-driven lip-sync, and face enhancement for improving photorealism.
Model inputs and outputs
The video-retalking model takes two inputs: a talking-head video file and an audio file. It then outputs a new video file where the face in the original video is lip-synced to the input audio.
Inputs
- Face: Input video file of a talking-head
- Input Audio: Input audio file to drive the lip-sync
Outputs
- Output Video: New video file with the face lip-synced to the input audio
Capabilities
The video-retalking model is capable of editing the faces in a video to match input audio, even if the original video and audio do not align. It can generate new facial animations with different expressions and emotions compared to the original video. The model is designed to work on "in the wild" videos without requiring manual alignment or preprocessing.
What can I use it for?
The video-retalking model can be used for a variety of video editing and content creation tasks. For example, you could use it to dub foreign language videos into your native language, or to animate a character's face to match pre-recorded dialogue. It could also be used to create custom talking-head videos for presentations, tutorials, or other multimedia content. Companies could leverage this technology to easily create personalized marketing or training videos.
Things to try
One interesting aspect of the video-retalking model is its ability to modify the expression of the face in the original video. By providing different expression templates, you can experiment with creating talking-head videos that convey different emotional states, like surprise or anger, even if the original video had a neutral expression. This could enable new creative possibilities for video storytelling and content personalization.
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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The video-retalking model is a powerful AI system developed by Tencent AI Lab researchers that can edit the faces of real-world talking head videos to match an input audio track, producing a high-quality and lip-synced output video. This model builds upon previous work in StyleHEAT, CodeTalker, SadTalker, and other related models. The key innovation of video-retalking is its ability to disentangle the task of audio-driven lip synchronization into three sequential steps: (1) face video generation with a canonical expression, (2) audio-driven lip-sync, and (3) face enhancement for improving photo-realism. This modular approach allows the model to handle a wide range of talking head videos "in the wild" without the need for manual alignment or other user intervention. Model inputs and outputs Inputs Face**: An input video file of someone talking Input Audio**: An audio file that will be used to drive the lip-sync Audio Duration**: The maximum duration in seconds of the input audio to use Outputs Output**: A video file with the input face modified to match the input audio, including lip-sync and face enhancement. Capabilities The video-retalking model can seamlessly edit the faces in real-world talking head videos to match new input audio, while preserving the identity and overall appearance of the original subject. This allows for a wide range of applications, from dubbing foreign-language content to animating avatars or CGI characters. Unlike previous models that require careful preprocessing and alignment of the input data, video-retalking can handle a variety of video and audio sources with minimal manual effort. The model's modular design and attention to photo-realism also make it a powerful tool for advanced video editing and post-production tasks. What can I use it for? The video-retalking model opens up new possibilities for creative video editing and content production. Some potential use cases include: Dubbing foreign language films or TV shows Animating CGI characters or virtual avatars with realistic lip-sync Enhancing existing footage with more expressive or engaging facial performances Generating custom video content for advertising, social media, or entertainment As an open-source model from Tencent AI Lab, video-retalking can be integrated into a wide range of video editing and content creation workflows. Creators and developers can leverage its capabilities to produce high-quality, lip-synced video outputs that captivate audiences and push the boundaries of what's possible with AI-powered media. Things to try One interesting aspect of the video-retalking model is its ability to not only synchronize the lips to new audio, but also modify the overall facial expression and emotion. By leveraging additional control parameters, users can experiment with adjusting the upper face expression or using pre-defined templates to alter the character's mood or demeanor. Another intriguing area to explore is the model's robustness to different types of input video and audio. While the readme mentions it can handle "talking head videos in the wild," it would be valuable to test the limits of its performance on more challenging footage, such as low-quality, occluded, or highly expressive source material. Overall, the video-retalking model represents an exciting advancement in AI-powered video editing and synthesis. Its modular design and focus on photo-realism open up new creative possibilities for content creators and developers alike.
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