Alibaba WAN 2.2 Animate: Next-Generation AI Model for Character Animation and Replacement
Introduction
With the rapid development of AI video generation technology, more and more models are beginning to support generating dynamic videos driven by static images. WAN 2.2 Animate (also known as Wan-Animate / Wan2.2-Animate) is one of the groundbreaking models in this field. Supported by the Alibaba background team, it builds upon the "Wan" series models, integrating character animation and character replacement functions, dedicated to making static characters "come alive" and seamlessly blend into existing scenes.
Background: WAN Model and Alibaba's AI Video Strategy
- WAN Model Introduction: Wan (also known as Wanx) is a series of models launched by Alibaba in the video/image generation direction, dedicated to promoting high-quality video generation and video understanding technology.
- WAN 2.1 / WAN 2.x Development: WAN 2.2 is an important upgrade version of the WAN series, with significant improvements in video generation quality, motion consistency, multimodal fusion, and other aspects.
- Alibaba's Open Source Strategy: Alibaba has announced the release of the open-source version of WAN 2.1 to support broader research community participation.
What is WAN 2.2 Animate / Wan-Animate
Wan-Animate: Unified Character Animation and Replacement with Holistic Replication is an important submodule of the WAN 2.2 system, with the core goal of uniformly solving character animation and character replacement problems.
Core Features
Dual Mode Support
- Animation Mode: Input static character images + reference videos to generate animations following actions and expressions.
- Replacement Mode: Naturally replace static characters into existing videos while ensuring lighting and environmental consistency.
Architecture Design
- Built on the Wan-I2V framework.
- Uses skeleton signals for motion driving.
- Uses implicit facial features for expression driving.
- Introduces the Relighting LoRA module to solve lighting fusion problems in replacement scenarios.
Performance Advantages
- Multiple metrics (SSIM, LPIPS, FVD, etc.) outperform existing open-source baselines.
- Shows stronger motion consistency and identity stability in subjective evaluation.
- Integrated animation and replacement, reducing model switching costs.
Limitations and Challenges
- High inference resource consumption.
- May still experience motion distortion or unnatural fusion in extremely complex environments.
Comparison with Related Models
- Compared with models like Animate Anyone / UniAnimate / VACE, WAN 2.2 Animate has advantages in motion consistency, facial expressions, and environmental fusion.
- Compared with UniAnimate-DiT, WAN 2.2 Animate has more complete motion expression and replacement functions.
- Compared to traditional keypoint-based methods, WAN 2.2 Animate uses diffusion models and Transformer architecture for more natural generation results.
Usage Guide / Practical Implementation
Online Usage (Recommended)
If you want a more convenient experience, visit wan-ai.tech directly for one-click online generation without downloading or installation.
Local Running
- Clone the repository and install dependencies (PyTorch, etc.).
- Download WAN 2.2 Animate model weights (such as Animate-14B).
Input Preparation
- Character Images: Portraits, illustrations, or cartoon characters.
- Reference Videos: Standard videos for driving actions and expressions.
- Replacement Mode: Requires preparation of videos to be replaced.
Inference Process
- Animation Mode: Run
generate.py and specify --task animate-14B.
- Replacement Mode: Use
--replace_flag with Relighting LoRA.
- Long Video Generation: Maintain continuity through temporal chaining.
Application Scenarios
- Character Animation: Dynamic illustration characters and virtual figures.
- Video Replacement: Natural face swapping and character replacement.
- Film & Advertising: Quickly generate character animation segments.
- Virtual Anchors: Create real-time animated virtual personas.
Future Prospects
- Inference Acceleration: Reduce memory and computational costs.
- Multimodal Extension: Combine audio-driven and text-driven approaches.
- High-Definition Long Video Support: Support higher resolution and longer duration.
- Interactive Enhancement: Increase controllability of actions, expressions, and camera angles.
- Real-time Applications: Apply to virtual live streaming and interactive scenarios.