Wan2.2-S2V: Audio-Driven Video Generation Model Released
Wan2.2-S2V is an AI video generation model that can convert static images and audio inputs into video content. The model can generate videos up to minute-level duration in a single generation, providing new solutions for video creation in digital human livestreaming, film production, and education industries.
The model performs well in film and television application scenarios, capable of generating facial expressions, body movements, and camera language. It supports full-body and half-body character generation, able to complete various content creation needs such as dialogue, singing, and performance.
Technical Features
Image + Audio = Video Generation Wan2.2-S2V uses a combination of image and audio input methods, generating video content through a static image and an audio clip. The model supports real people, cartoons, animals, digital humans, and other types of images, and supports portrait, half-body, and full-body formats. After uploading an audio clip, the model can make the main subject in the image perform actions such as speaking, singing, and performing.
Audio-Driven Video Generation The model can generate videos based on audio input, supporting dialogue and narrative scene generation. Through audio input, the model can control character lip-sync, expressions, and movements, achieving audio-video synchronization.
Text Control Function Wan2.2-S2V also supports text control, allowing control of video scenes through Prompt input, enabling changes in video subject movements and backgrounds. For example, by uploading a photo of someone playing piano, a song, and a text description, the model can generate a piano performance video, maintaining character consistency with the original image, synchronizing facial expressions and mouth movements with audio, and matching finger movements to audio rhythm.
Technical Architecture
Wan2.2-S2V is based on Tongyi Wanxiang video generation foundation model, combining text-guided global motion control and audio-driven local motion control to achieve audio-driven video generation. The model adopts AdaIN and CrossAttention control mechanisms to enhance audio control effects.
To support long video generation, Wan2.2-S2V uses hierarchical frame compression technology to reduce the token count of historical frames, extending motion frames (historical reference frames) from several frames to 73 frames, achieving stable long video generation.
In training, the team built a dataset of over 600,000 audio-video segments, using mixed parallel training for full parameter training. The model supports multi-resolution training and inference, adapting to different resolution video generation requirements.
Performance Metrics
Test data shows that Wan2.2-S2V performs well across multiple evaluation metrics:
- FID (Video Quality): 15.66
- EFID (Expression Authenticity): 0.283
- CSIM (Identity Consistency): 0.677
- SSIM (Structural Similarity): 0.734
- PSNR (Peak Signal-to-Noise Ratio): 20.49
These metrics indicate that Wan2.2-S2V performs well in video quality, expression authenticity, and identity consistency.
Application Scenarios
Wan2.2-S2V is suitable for various professional content creation scenarios:
- Film Production: Supports movie dialogue and narrative scene generation
- Music Videos: Can generate synchronized music performance videos based on audio
- Educational Content: Supports automated generation of educational videos
- Entertainment Content: Applicable to various entertainment and performance video creation
Technical Characteristics
The main technical features of Wan2.2-S2V include:
- Audio-Video Synchronization: Achieves audio-video synchronization through audio processing pipeline
- Expression and Movement Generation: Can generate facial expressions and body movements
- Camera Control: Supports different camera angles and lens language
- Multi-Resolution Support: Adapts to different resolution video generation requirements
Open Source and Experience
Open Source Addresses:
Experience Addresses:
Wan2.2-S2V provides new technical solutions for the AI video generation field, offering content creators tools for audio-driven video generation. The model has application potential in film production, music video production, and other fields.