🎬 video

LatentSync

Lip synchronization framework based on audio-conditioned latent diffusion model

#video production
#Stable Diffusion
#latent diffusion model
#lip sync
#Audio and video processing
#TREPA
LatentSync

Product Details

LatentSync is a lip sync framework developed by ByteDance based on the latent diffusion model of audio conditions. It directly leverages the power of Stable Diffusion to model complex audio-visual correlations without any intermediate motion representation. This framework effectively improves the temporal consistency of generated video frames while maintaining the accuracy of lip synchronization through the proposed temporal representation alignment (TREPA) technology. This technology has important application value in fields such as video production, virtual anchoring, and animation production. It can significantly improve production efficiency, reduce labor costs, and bring users a more realistic and natural audio-visual experience. The open source nature of LatentSync also enables it to be widely used in academic research and industrial practice, promoting the development and innovation of related technologies.

Main Features

1
Audio-conditioned latent diffusion models: Leverage Stable Diffusion to directly model audio-video correlations without intermediate motion representations
2
Temporal Representation Alignment (TREPA): Enhancing the temporal consistency of generated video frames through temporal representations extracted by large-scale self-supervised video models
3
High lip synchronization accuracy: through optimization methods such as SyncNet loss, ensure the lip synchronization effect of the generated video
4
Complete data processing process: Provides a complete data processing script, covering steps such as video restoration, frame rate resampling, scene detection, face detection and alignment, etc.
5
Open source training and inference code: including training scripts for U-Net and SyncNet, as well as inference scripts to facilitate user model training and application
6
Model checkpoint provides: checkpoint files of open source models to facilitate users to quickly download and use
7
Supports multiple video styles: capable of processing different styles of video materials such as real videos and animation videos

How to Use

1
1. Environment preparation: Install the required dependency packages and download the model checkpoint file. The specific steps are to run the setup_env.sh script.
2
2. Data processing: Use the data_processing_pipeline.sh script to preprocess video data, including video repair, frame rate resampling, scene detection, face detection and alignment, etc.
3
3. Model training: If you need to train a model, you can run the train_unet.sh and train_syncnet.sh scripts to train U-Net and SyncNet respectively.
4
4. Inference usage: Run the inference.sh script to generate lip synchronization videos. You can adjust the guidance_scale parameter as needed to improve the accuracy of lip synchronization.
5
5. Result evaluation: Evaluate the generated lip synchronization video to check how well the lip movements match the speech, as well as the overall quality and effect of the video.

Target Users

It is suitable for professionals such as video producers, animators, virtual anchor developers, game developers, film and television special effects artists who need to perform lip synchronization, as well as academic researchers and enthusiasts who are interested in lip synchronization technology.

Examples

When making virtual anchor videos, use LatentSync to automatically generate realistic lip movements based on the anchor's voice, making the video more realistic and interactive.

Animation production companies can use LatentSync to automatically generate matching lip animations when dubbing characters, saving the time and cost of traditional manual lip animation production.

When producing special effects videos, film and television special effects teams can use LatentSync to repair or enhance the lip synchronization effect of the characters in the video to improve the overall visual effect.

Quick Access

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Categories

🎬 video
› AI model
› video generation

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