Installation and Usage Guide
This guide provides information on accessing and using AnyTalker for multi-person talking video generation. Learn about the framework's capabilities, technical requirements, and how to get started.
About AnyTalker
AnyTalker is a multi-person talking video generation framework that creates natural, interactive talking videos driven by audio. The framework features an extensible multi-stream processing architecture with identity-aware attention mechanism, enabling the generation of videos with multiple speakers displaying coherent interactions.
Key Components
Architecture Overview
AnyTalker is built on several key technical components:
- Diffusion Transformer Base: Provides the foundation for high-quality video generation
- Identity-Aware Attention: Distinguishes between different people in the scene
- Multi-Stream Processing: Handles multiple identity-audio pairs through parallel streams
- Interactivity Refinement: Ensures natural social dynamics between speakers
System Requirements
For running multi-person talking video generation, consider the following requirements:
- GPU with sufficient VRAM for video generation tasks
- Python 3.8 or higher
- CUDA support for GPU acceleration
- Adequate disk space for model weights and generated videos
- Libraries: PyTorch, Diffusers, and related dependencies
Resource Considerations
Multi-person video generation has specific resource requirements:
- Computational requirements scale with the number of identities
- More speakers require more processing power and generation time
- Video resolution and duration affect memory requirements
- GPU memory should accommodate model parameters and video buffers
Research Paper and Resources
AnyTalker is described in detail in the research paper published on arXiv. The paper provides comprehensive information about the framework's architecture, training approach, and evaluation results.
Official Resources
- Research Paper: Available on arXiv (arXiv:2511.23475)
- Project Homepage: HKUST-C4G AnyTalker Homepage
- Video Demonstrations: Available on YouTube
- Authors: Zhizhou Zhong, Yicheng Ji, Zhe Kong, and team
Framework Capabilities
Multi-Person Generation
Generate videos with multiple speakers simultaneously. The framework can handle an arbitrary number of identities by adding processing streams for each additional person.
Audio-Driven Animation
Drive each person with their own audio track. The system generates facial animations that accurately match the audio input, including lip synchronization and appropriate expressions.
Natural Interactivity
Generate videos with natural social dynamics between speakers. The interactivity refinement process ensures appropriate listening behaviors, eye contact, and reactions.
Scalable Architecture
The extensible architecture supports scaling from two-person conversations to larger group discussions without requiring architectural changes or retraining.
Training Approach
AnyTalker employs an innovative training strategy that reduces the need for expensive multi-person data collection:
Two-Phase Training
- Phase 1 - Single-Person Learning: The framework learns speaking patterns from abundant single-person videos, understanding how faces move during speech and how lip movements correspond to sounds.
- Phase 2 - Interactivity Refinement: Using only a few multi-person clips, the model refines its understanding of social dynamics and natural interactions between speakers.
Technical Features
| Feature | Description |
|---|---|
| Framework Type | Multi-Person Talking Video Generation |
| Architecture | Extensible Multi-Stream with Diffusion Transformer |
| Key Innovation | Identity-Aware Attention Mechanism |
| Scalability | Arbitrary Number of Drivable Identities |
| Input Type | Audio-Driven or Multi-Modal Signals |
| Output Quality | High Visual Quality with Natural Interactivity |
| Training Data | Primarily Single-Person, Refined with Few Multi-Person Clips |
Applications
AnyTalker enables various applications in multi-person video generation:
- Content Creation: Generate multi-person video content for social media and entertainment
- Education: Create interactive educational materials with multiple instructors
- Virtual Meetings: Develop prototypes and demonstrations of virtual collaboration tools
- Gaming: Animate multiple game characters with natural conversations
- Film Production: Assist in pre-visualization with multi-person scenes
- Research: Advance understanding of social dynamics and communication patterns
Performance Metrics
The framework has been evaluated on specialized metrics for multi-person talking video generation:
- Lip Synchronization: Accurate mouth movements matching audio for all speakers
- Visual Quality: High-quality facial animations and realistic appearance
- Interactivity Naturalness: Appropriate social behaviors and dynamics between speakers
- Identity Scalability: Consistent performance with varying numbers of speakers
Getting Started
To learn more about AnyTalker and access the research materials:
- Read the research paper on arXiv (arXiv:2511.23475) for detailed technical information
- Visit the official project homepage for resources and updates
- Watch video demonstrations to see the framework's capabilities
- Explore the technical documentation for implementation details
- Review the contributed dataset and metrics for evaluation
Research Citation
If you use AnyTalker in your research, please cite the paper:
@article{anytalker2025,
title={AnyTalker: Scaling Multi-Person Talking Video Generation with Interactivity Refinement},
author={Zhong, Zhizhou and Ji, Yicheng and Kong, Zhe and others},
journal={arXiv preprint arXiv:2511.23475},
year={2025}
}Additional Information
For the most up-to-date information about AnyTalker, including code availability, model weights, and technical documentation, please refer to the official project homepage and research paper. The project represents ongoing research in multi-person talking video generation.
Note: This guide provides an overview of AnyTalker technology. For specific implementation details, code access, and the latest updates, please consult the official research paper and project resources.