video-retalking
Maintainer: xiankgx
5.7K
| 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 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.
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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video-retalking
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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.
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video-retalking
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video-retalking is a system developed by researchers at Tencent AI Lab and Xidian University that enables audio-based lip synchronization and expression editing for talking head videos. It builds on prior work like Wav2Lip, PIRenderer, and GFP-GAN to create a pipeline for generating high-quality, lip-synced videos from talking head footage and audio. Unlike models like voicecraft, which focus on speech editing, or tokenflow, which aims for consistent video editing, video-retalking is specifically designed for synchronizing lip movements with audio. Model inputs and outputs video-retalking takes two main inputs: a talking head video and an audio file. The model then generates a new video with the facial expressions and lip movements synchronized to the provided audio. This allows users to edit the appearance and emotion of a talking head video while preserving the original audio. Inputs Face**: Input video file of a talking-head. Input Audio**: Input audio file to synchronize with the video. Outputs Output**: The generated video with synchronized lip movements and expressions. Capabilities video-retalking can generate high-quality, lip-synced videos even in the wild, meaning it can handle real-world footage without the need for extensive pre-processing or manual alignment. The model is capable of disentangling the task into three key steps: generating a canonical face expression, synchronizing the lip movements to the audio, and enhancing the photo-realism of the final output. What can I use it for? video-retalking can be a powerful tool for content creators, video editors, and anyone looking to edit or enhance talking head videos. Its ability to preserve the original audio while modifying the visual elements opens up possibilities for a wide range of applications, such as: Dubbing or re-voicing videos in different languages Adjusting the emotion or expression of a speaker Repairing or improving the lip sync in existing footage Creating animated avatars or virtual presenters Things to try One interesting aspect of video-retalking is its ability to control the expression of the upper face using pre-defined templates like "smile" or "surprise". This allows for more nuanced expression editing beyond just lip sync. Additionally, the model's sequential pipeline means each step can be examined and potentially fine-tuned for specific use cases.
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livespeechportraits
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The sadtalker-video model, developed by Gaurav Kohli, is a video lip synchronization model that can generate talking head videos from audio input. It builds upon the work of the SadTalker and VideoReTalking models, which focused on audio-driven single image and video talking face animation respectively. Model inputs and outputs The sadtalker-video model takes two inputs: an audio file (.wav or .mp4) and a source video file (.mp4). The model can then generate a synchronized talking head video, with the option to enhance the lip region or the entire face. Additionally, the model can use Depth-Aware Video Frame Interpolation (DAIN) to increase the frame rate of the output video, resulting in smoother transitions. Inputs Audio Input Path**: The path to the audio file (.wav or .mp4) that will drive the lip movements. Video Input Path**: The path to the source video file (.mp4) that will be used as the base for the lip-synced output. Use DAIN**: A boolean flag to enable or disable Depth-Aware Video Frame Interpolation, which can improve the smoothness of the output video. Enhancer Region**: The area of the face to be enhanced, with options for "lip", "face", or "none". Outputs Output**: The path to the generated lip-synced video file. Capabilities The sadtalker-video model can generate realistic lip-synced talking head videos from audio and source video input. It offers several enhancements, such as the ability to focus the enhancement on the lip region or the entire face, and the option to use DAIN to improve the smoothness of the output. What can I use it for? The sadtalker-video model can be used for a variety of applications, such as video dubbing, virtual assistants, and animated videos. It can be particularly useful for creating personalized content, enhancing existing videos, or generating synthetic media for various use cases. Things to try One interesting aspect of the sadtalker-video model is the ability to selectively enhance different regions of the face. You could experiment with the different enhancement options to see how they affect the quality and realism of the generated videos. Additionally, trying out the DAIN feature can help you understand how it impacts the smoothness and transitions in the output.
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