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FantasyPortrait: Enhancing Multi-Character Portrait Animation with Expression-Augmented Diffusion Transformers

Qiang Wang, Mengchao Wang, Fan Jiang, Yaqi Fan, Yonggang Qi, Mu Xu

2025-07-18

FantasyPortrait: Enhancing Multi-Character Portrait Animation with
  Expression-Augmented Diffusion Transformers

Summary

This paper talks about FantasyPortrait, a new AI system that creates realistic and expressive facial animations for portraits of one or more characters using advanced diffusion transformer technology.

What's the problem?

The problem is that making facial animations that show rich emotions and work well for multiple characters at once is very difficult, especially when trying to keep the animations smooth and natural.

What's the solution?

The authors developed a diffusion transformer framework that uses implicit representations to capture detailed facial features and a special masked cross-attention mechanism to focus on different characters and their expressions. This allows the system to generate high-quality, emotionally clear animations.

Why it matters?

This matters because it enables better and more lifelike animated portraits, which can be used in games, movies, virtual reality, and other creative fields where expressive character animation is important.

Abstract

FantasyPortrait, a diffusion transformer framework, generates high-fidelity and emotion-rich facial animations for single and multi-character scenarios using implicit representations and a masked cross-attention mechanism.