What is UniAVGen?
UniAVGen is a unified framework for high-fidelity joint audio-video generation. It addresses key limitations found in existing methods, including poor lip synchronization, insufficient semantic consistency, and limited task generalization. The system creates temporally aligned audio and video content that maintains strong semantic relationships between the two modalities.
At its core, UniAVGen adopts a symmetric dual-branch architecture with parallel Diffusion Transformers for audio and video processing. This design enables the framework to build a cohesive cross-modal latent space where audio and video information can interact effectively. The architecture ensures that both modalities are processed with equal importance, allowing for balanced generation of synchronized content.
The framework introduces three critical innovations that set it apart from previous approaches. First, the Asymmetric Cross-Modal Interaction mechanism enables bidirectional temporal alignment between audio and video. This means the system can align audio to video and video to audio, ensuring precise spatiotemporal synchronization. Second, Face-Aware Modulation dynamically prioritizes salient facial regions during the interaction process, focusing computational resources on areas that matter most for synchronization. Third, Modality-Aware Classifier-Free Guidance amplifies cross-modal correlation signals during inference, improving the quality of generated content.
UniAVGen achieves these results with far fewer training samples compared to other methods. While some approaches require over 30 million training samples, UniAVGen delivers superior performance with just 1.3 million samples. This efficiency makes the framework more accessible and practical for real-world applications.
Overview of UniAVGen
| Feature | Description |
|---|---|
| Framework Name | UniAVGen |
| Category | Unified Audio-Video Generation |
| Architecture | Symmetric Dual-Branch with Diffusion Transformers |
| Key Innovation | Asymmetric Cross-Modal Interaction |
| Training Efficiency | 1.3M training samples |
| Primary Function | Joint Audio-Video Generation with Synchronization |
Core Architecture and Technology
UniAVGen's architecture consists of two parallel Diffusion Transformers, one for audio and one for video. These transformers work together to create a unified representation space where both modalities can interact. The dual-branch design ensures that neither audio nor video dominates the generation process, maintaining balance throughout.
The Asymmetric Cross-Modal Interaction mechanism is the heart of UniAVGen's synchronization capabilities. This system allows audio and video to influence each other bidirectionally, meaning audio can guide video generation and video can guide audio generation. The interaction happens at multiple temporal scales, ensuring that both short-term and long-term synchronization are maintained.
Face-Aware Modulation adds another layer of sophistication to the framework. During cross-modal interactions, this module identifies and prioritizes facial regions that are most important for synchronization. By focusing computational resources on these areas, UniAVGen achieves better lip synchronization and emotional expression alignment between audio and video.
Modality-Aware Classifier-Free Guidance enhances the generation process during inference. This technique amplifies the signals that indicate strong cross-modal correlations, helping the model produce content where audio and video are well-aligned. The guidance mechanism is aware of both modalities, allowing it to make informed decisions about how to balance audio and video generation.
Demo Videos
Key Features of UniAVGen
Asymmetric Cross-Modal Interaction
Enables bidirectional temporal alignment between audio and video, ensuring precise synchronization at multiple time scales. The system allows audio to influence video generation and vice versa, creating a cohesive relationship between the two modalities.
Face-Aware Modulation
Dynamically prioritizes salient facial regions during cross-modal interactions, focusing on areas most critical for lip synchronization and emotional expression. This targeted approach improves the quality of generated content while maintaining computational efficiency.
Modality-Aware Classifier-Free Guidance
Amplifies cross-modal correlation signals during inference, helping the model produce well-aligned audio and video content. The guidance mechanism considers both modalities when making generation decisions.
Dual-Branch Architecture
Uses parallel Diffusion Transformers for audio and video processing, creating a balanced framework where both modalities receive equal attention. This design prevents one modality from dominating the generation process.
Multi-Task Capability
Supports multiple audio-video generation tasks within a single unified framework, including joint generation, continuation, dubbing, and audio-driven synthesis. This versatility makes UniAVGen practical for various applications.
Training Efficiency
Achieves superior performance with significantly fewer training samples compared to other methods. The framework requires only 1.3 million samples while delivering better results than systems trained on 30 million samples.
Multi-Task Capabilities
1. Joint Audio-Video Generation
This task involves creating synchronized audio and video from basic inputs. The system takes a reference image, video caption, and speech content as input. From these elements, UniAVGen generates temporally aligned audio and video that maintain strong semantic consistency.
The generated content demonstrates emotion consistency across different emotional states. The framework can produce happy, calm, and angry expressions while maintaining proper synchronization between audio and video. This capability is essential for creating natural-looking content where facial expressions match the emotional tone of the audio.
2. Joint Generation with Reference Audio
This capability allows users to control the timbre of generated audio by providing a reference audio sample. The system takes a reference image, video caption, speech content, and reference audio for timbre control as inputs.
The output consists of aligned audio-video content where the audio timbre matches the reference audio. This feature is useful for maintaining consistent voice characteristics across different generations or matching specific voice qualities.
3. Joint Audio-Video Continuation
This task enables the framework to continue existing audio and video content. The system takes a reference image, video caption, speech content, and conditional audio or video as inputs. The conditional content typically represents the first portion of the sequence that needs to be continued.
The output is a continuation of the audio and video that preserves temporal consistency through cross-modal interaction. The framework ensures that the continuation matches the style, timing, and characteristics of the conditional content, creating a natural extension of the original sequence.
4. Video-to-Audio Dubbing
This capability generates audio that matches existing video content. The system takes a conditional silent video, video caption, speech content, and optional reference audio as inputs.
The output is audio that aligns with the video's emotions and expressions. The framework analyzes the video content to understand the emotional context and generates appropriate audio that matches the visual cues. This is particularly useful for adding dialogue or narration to silent videos.
5. Audio-Driven Video Synthesis
This task creates video content that responds to audio input. The system takes a reference image, video caption, and conditional audio as inputs.
The output is video with expressions and motions aligned to the driving audio. The framework analyzes the audio to extract emotional and rhythmic information, then generates corresponding video content. This capability is useful for creating talking head videos or animated content that responds to audio cues.
Applications and Use Cases
UniAVGen's capabilities make it suitable for various applications across different industries. In content creation, the framework can generate synchronized audio and video for videos, presentations, and multimedia projects. The ability to maintain lip synchronization makes it particularly valuable for creating talking head videos and virtual avatars.
The video-to-audio dubbing capability enables content creators to add narration or dialogue to existing video content. This is useful for localizing content for different languages or adding voiceovers to silent videos. The emotion consistency ensures that the added audio matches the emotional tone of the video.
Audio-driven video synthesis opens possibilities for creating animated content that responds to audio input. This can be used for creating virtual presenters, animated characters, or interactive content where video generation is driven by audio cues. The framework's ability to maintain synchronization ensures that the generated video accurately reflects the audio content.
The continuation capability allows for extending existing audio-video content while maintaining consistency. This is useful for creating longer sequences from shorter samples or extending content while preserving the original style and characteristics. The cross-modal interaction ensures that both audio and video continue in a coordinated manner.
Technical Advantages
UniAVGen offers several technical advantages over existing methods. The framework's training efficiency is particularly notable, requiring only 1.3 million training samples compared to the 30.1 million samples needed by some alternative approaches. This efficiency makes the framework more accessible and reduces the computational resources required for training.
The bidirectional cross-modal interaction ensures that audio and video influence each other throughout the generation process. This creates stronger synchronization than unidirectional approaches where one modality simply follows the other. The temporal alignment mechanism works at multiple scales, ensuring both short-term and long-term consistency.
Face-Aware Modulation improves the quality of generated content by focusing on the most important regions. This targeted approach is more efficient than processing all regions equally, and it produces better results for tasks requiring lip synchronization and emotional expression alignment.
The unified framework design allows multiple tasks to be handled by a single model. This reduces the need for separate models for different tasks and simplifies the overall system architecture. The multi-task capability also means that improvements to the core framework benefit all supported tasks.
Performance and Results
Comprehensive experiments validate UniAVGen's performance across multiple dimensions. The framework demonstrates overall advantages in audio-video synchronization, timbre consistency, and emotion consistency compared to existing methods. These improvements are achieved with significantly fewer training samples, making the framework both more effective and more efficient.
The synchronization capabilities are particularly strong, with the framework maintaining precise temporal alignment between audio and video across different scenarios. This includes maintaining lip synchronization for talking head videos and ensuring that emotional expressions match the audio content.
Timbre consistency is another area where UniAVGen excels. When using reference audio for timbre control, the framework successfully maintains consistent voice characteristics across different generations. This capability is important for creating content with consistent voice qualities.
Emotion consistency is maintained across different emotional states, with the framework generating appropriate facial expressions and audio tones for happy, calm, and angry emotions. The cross-modal interaction ensures that both audio and video reflect the intended emotional state consistently.
Pros and Cons
Pros
- Superior audio-video synchronization
- Strong semantic consistency between modalities
- Training efficiency with fewer samples required
- Multi-task capability in unified framework
- Bidirectional cross-modal interaction
- Face-aware processing for better lip sync
- Emotion consistency across different states
- Timbre control with reference audio
Cons
- Requires reference image or video for generation
- Computational resources needed for dual-branch processing
- Performance may vary with input quality
- Training still requires substantial data preparation
How UniAVGen Works
Step 1: Input Preparation
The process begins with preparing the necessary inputs. Depending on the task, this may include a reference image, video caption, speech content, conditional audio or video, and optional reference audio. The system processes these inputs to extract relevant features for generation.
Step 2: Dual-Branch Processing
The inputs are processed through parallel Diffusion Transformers, one for audio and one for video. These transformers work simultaneously to build representations of both modalities in a shared latent space. The parallel processing ensures balanced attention to both audio and video.
Step 3: Cross-Modal Interaction
The Asymmetric Cross-Modal Interaction mechanism enables bidirectional communication between the audio and video branches. Audio features influence video generation, and video features influence audio generation. This interaction happens at multiple temporal scales to ensure comprehensive synchronization.
Step 4: Face-Aware Modulation
During cross-modal interactions, the Face-Aware Modulation module identifies and prioritizes facial regions that are most important for synchronization. This targeted approach focuses computational resources on areas critical for lip synchronization and emotional expression alignment.
Step 5: Modality-Aware Guidance
The Modality-Aware Classifier-Free Guidance mechanism amplifies cross-modal correlation signals during the generation process. This helps the model produce content where audio and video are well-aligned, improving the overall quality of the generated content.
Step 6: Output Generation
The final step involves generating the synchronized audio and video output. The framework ensures temporal alignment and semantic consistency between the two modalities, producing high-quality content that maintains proper synchronization throughout.