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opentalker/video-retalking

7,250PythonAudience · researcherComplexity · 4/5Setup · hard

TLDR

VideoReTalking is a research tool that replaces the lip movements in a talking-head video to match a new audio track, making a person appear to say completely different words.

Mindmap

mindmap
  root((VideoReTalking))
    What it does
      Lip-sync replacement
      Audio-driven editing
      Talking head video
    Processing Pipeline
      Expression normalization
      Lip movement synthesis
      Face enhancement
    Inputs and Outputs
      Source face video
      New audio file
      Expression templates
    Setup
      Python and PyTorch
      CUDA GPU required
      Pre-trained models
      Google Colab option
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Things people build with this

USE CASE 1

Dub a talking-head video into a different language by syncing new audio to the speaker's lip movements

USE CASE 2

Make a recorded speaker appear to deliver different words than what they originally said

USE CASE 3

Research and benchmark lip-sync quality by testing different audio inputs against the same source video

Tech stack

PythonPyTorchCUDA

Getting it running

Difficulty · hard Time to first run · 1h+

Requires a CUDA-capable GPU, PyTorch with CUDA 11.1, and manual download of pre-trained model weights before the first run.

License not mentioned in the explanation.

In plain English

VideoReTalking is a research tool that takes an existing video of a person talking and replaces their lip movements to match a different audio track. The result is a video where the person appears to be saying whatever words you provide in the audio, even if the original video had completely different speech. This was published as a research paper at SIGGRAPH Asia 2022, a major conference for computer graphics. The tool works in three steps that run one after another without requiring manual work between them. First, it adjusts the facial expressions in each frame of the video to a neutral baseline so that the lip-sync step has a consistent starting point. Second, it uses a separate model that takes that normalized video along with your new audio and generates new lip movements that match the sounds. Third, a final step cleans up the result, sharpening the face region and making it look more photorealistic. To use it, you provide a video of a face and an audio file, and the tool produces a new video with the lips resynced. You can also influence the expression of the output by choosing templates like neutral or smile, or by modifying the upper face region with options like surprised or angry. Setup requires Python, PyTorch, and a CUDA-capable graphics card. The instructions are written for CUDA 11.1, and you also need to download pre-trained model files separately before running. A Google Colab notebook is available if you want to try it without setting up a local environment. The code was produced by researchers at Xidian University and Tencent AI Lab. It runs entirely offline and does not send data anywhere. The repository also points to several related projects that work on similar problems, such as generating talking head animations from a single still image.

Copy-paste prompts

Prompt 1
I have a 30-second video of a person speaking and a new WAV audio file with different words. Walk me through the VideoReTalking command to produce the lip-synced output video.
Prompt 2
How do I set up the VideoReTalking Python environment on Ubuntu with CUDA 11.1, and where do I download the required pre-trained model checkpoint files?
Prompt 3
I don't have a local GPU. Show me how to use the VideoReTalking Google Colab notebook to upload my video and audio files and download the lip-synced result.
Prompt 4
Explain VideoReTalking's three processing stages, what does each stage do to the video and why is expression normalization needed before lip synthesis?
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