LatentSync: Audio Conditioned Latent Diffusion Models for Lip Sync

What is the project about?

LatentSync is an end-to-end lip-sync framework that generates lip-synced videos directly from audio input. It uses a novel approach based on audio-conditioned latent diffusion models, leveraging the capabilities of Stable Diffusion.

What problem does it solve?

The project addresses the problem of generating realistic and temporally consistent lip-synced videos. Existing diffusion-based methods often suffer from temporal inconsistencies. LatentSync aims for higher quality and more stable lip movements synchronized with audio.

What are the features of the project?

• End-to-end lip-sync generation: Directly generates video frames from audio, without relying on intermediate representations like facial landmarks.
• Audio-conditioned latent diffusion model: Uses a diffusion model in the latent space of Stable Diffusion, conditioned on audio embeddings from Whisper.
• Temporal REPresentation Alignment (TREPA): A novel technique to improve temporal consistency in the generated video by aligning frame representations with those from large-scale self-supervised video models.
• High-quality output: Leverages Stable Diffusion to produce visually appealing and realistic results.
• Open-source: Includes inference code, data processing pipeline, and training code.

What are the technologies used in the project?

• Stable Diffusion: A powerful latent diffusion model for image generation.
• Whisper: An audio encoder from OpenAI used to extract audio embeddings.
• U-Net: The core architecture of the diffusion model, modified to incorporate audio conditioning via cross-attention.
• SyncNet: A pre-trained model used to enforce lip-sync accuracy by providing a loss function during training.
• LPIPS (Learned Perceptual Image Patch Similarity): A perceptual loss function used to improve visual quality.
• TREPA: Custom temporal consistency module.
• PyTorch: Deep learning framework.
• Gradio: For creating a user-friendly demo application.
• PySceneDetect: For scene detection in the data processing pipeline.
• face-alignment: For face landmark detection.

What are the benefits of the project?

• Improved temporal consistency: TREPA significantly enhances the stability of lip movements across frames.
• High-quality lip-sync: Achieves accurate synchronization between audio and video.
• End-to-end approach: Simplifies the lip-sync generation process.
• Open-source and reproducible: Provides code and resources for others to use and build upon.
• Easy to use: Includes a Gradio app and command-line interface for inference.

What are the use cases of the project?

• Dubbing: Automatically generating lip-synced videos for different languages.
• Virtual avatars: Creating realistic talking avatars for games, virtual assistants, and other applications.
• Video editing: Modifying the lip movements in existing videos.
• Accessibility: Improving communication for individuals with speech impairments.
• Animation: Generating lip-sync for animated characters.
• Content Creation: Creating engaging video content with synchronized audio and visuals.