About AnyTalker

Welcome to AnyTalker, a multi-person talking video generation framework designed to create natural and interactive talking videos. AnyTalker represents a significant advancement in the field of audio-driven facial animation, enabling the generation of videos featuring multiple speakers with coherent interactions and social dynamics.

What is AnyTalker?

AnyTalker is a framework that generates talking videos featuring multiple people simultaneously. Traditional talking head systems typically animate a single person, but AnyTalker extends this capability to handle multiple speakers in the same scene. Each person can be driven by their own audio track, creating natural conversations and interactions between speakers.

The framework is built on an extensible multi-stream processing architecture that can handle an arbitrary number of identities. This means you can animate two people having a conversation, a small group discussion, or even larger gatherings, all with the same underlying technology. The system coordinates the movements and expressions of all speakers to create believable multi-person scenes.

Key Innovation: Identity-Aware Attention

At the heart of AnyTalker is the identity-aware attention mechanism, which extends the Diffusion Transformer architecture. This mechanism allows the model to distinguish between different people in the scene while processing their facial animations. It can focus on specific identities while maintaining awareness of others, enabling independent yet coordinated animation of multiple speakers.

The identity-aware attention mechanism iteratively processes identity-audio pairs, building up the multi-person scene gradually. Each iteration adds another person while considering the positions and states of previously processed identities. This approach ensures that all speakers display appropriate social behaviors and natural interactions.

Technical Architecture

AnyTalker's architecture consists of several key components:

Efficient Training Approach

One of the most innovative aspects of AnyTalker is its training strategy. Traditional multi-person video generation systems require massive amounts of multi-person training data, which is expensive and time-consuming to collect. AnyTalker takes a different approach.

The framework learns speaking patterns primarily from single-person videos, which are much more abundant and easier to collect. These videos teach the model how faces move during speech, how lip movements correspond to sounds, and how expressions change. After learning individual speaking patterns, AnyTalker refines its understanding of interactivity using only a small number of real multi-person clips.

This two-phase training approach dramatically reduces the need for expensive multi-person data collection. By depending primarily on single-person videos, the framework makes multi-person talking video generation more practical and accessible.

Key Capabilities

• Multi-person video generation with natural coordination between speakers
• Scalable architecture supporting arbitrary number of identities
• Remarkable lip synchronization across all speakers
• Natural social interactions and dynamics between speakers
• High visual quality with realistic facial movements
• Audio-driven animation using voice recordings or synthesized speech
• Efficient training using primarily single-person data
• Identity-aware attention for coordinated multi-person animation

Research Background

AnyTalker was developed by a team of researchers focused on advancing multi-person video generation technology. The project addresses fundamental challenges in creating believable multi-person talking videos, including the difficulty of coordinating multiple speakers and the high cost of collecting diverse multi-person training data.

The research has been published and presented to the academic community, contributing to the broader understanding of talking head generation. The team has also contributed evaluation metrics and a specialized dataset for multi-person talking video generation, helping establish benchmarks for comparing different approaches.

Applications

AnyTalker has wide-ranging applications across various domains:

• Content Creation: Generate engaging multi-person video content for social media, education, or entertainment
• Education and Training: Create interactive educational content featuring multiple instructors or characters
• Virtual Meetings: Generate synthetic participants for demonstrations or prototypes
• Gaming and Animation: Animate multiple characters using voice acting as input
• Film and Media Production: Assist in pre-visualization and storyboarding
• Research and Development: Advance research in computer vision and human-computer interaction

Technical Performance

AnyTalker has been evaluated extensively on metrics designed to assess multi-person talking video generation. The evaluations demonstrate remarkable lip synchronization, high visual quality, and natural interactivity between generated speakers. The framework strikes a favorable balance between data costs and identity scalability.

Future Directions

The AnyTalker framework provides a foundation for future advancements in multi-person video generation. Potential areas of development include improved interaction modeling, support for larger groups, enhanced visual quality, and integration with other modalities such as body language and gestures.

Research Resources

• Research Paper: Available on arXiv (arXiv:2511.23475)
• Project Homepage: HKUST-C4G AnyTalker Homepage
• Video Demonstrations: Available on YouTube
• Dataset and Metrics: Contributed for multi-person video evaluation