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Wan 2.2 14B Animate Move

Developed by Alibaba as part of the Wan 2.2 family, it is widely used for AI avatars, virtual influencers, and animation production acceleration.
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Wan 2.2 14B Animate Move

Wan 2.2 14B Animate Move is an advanced AI model designed to animate static images by transferring movements and facial expressions from a reference video.

Wan 2.2 14B Animate Move is a large-scale AI video generation model designed specifically for controllable animation of static character images by transferring movements and expressions from a reference video. It enables users to upload a still photo of a character and a drive video with the desired motions. The system extracts poses and masks, then animates the character in one of two modes, with the Animation mode focusing on creating a new video by applying the movements and expressions from the drive video onto the static photo, producing a video where the character mimics the same gestures and angles.

Technical Specifications

  • Model Size: 14 billion parameters (generation backbone)
  • Architecture: Diffusion transformer model with a mixture-of-experts (MoE) design for increased capacity without extra computational cost
  • Training Objective: Flow matching with diffusion-style denoising in a compact 3D spatio-temporal latent space
  • Attention Mechanism: Pooled spatio-temporal self-attention across frames and pixels, plus cross-attention to text features (optional)
  • Inputs: Reference image (static character photo) + reference video (motion drive)
  • Output: High-quality 720p videos at 24 fps with character animation replicating the reference video’s movements and expressions

Performance Benchmarks

  • Successfully tested on high-end GPUs like NVIDIA H100 (80GB) with recommended VRAM of ~75 GB for extended sequences
  • Capable of producing coherent, high-quality videos with natural-looking character motions and expressions
  • Demonstrates robust identity preservation from a single reference image during dynamic motion transfer
  • Optimized for Ubuntu and CUDA-enabled environments with modern PyTorch stacks
  • Handles video lengths suitable for social media clips and short animated content effectively

Key Features

  • Animates static images using live motion from reference videos, transferring both body and facial expressions precisely
  • Mixture-of-experts architecture enables handling complex motions and detailed expression mapping without added compute cost
  • High temporal stability in motion thanks to a causal 3D compression method, preventing artifacts caused by future frame leakage
  • Supports realistic integration of animated characters with surroundings, controlling lighting and color to match backgrounds dynamically
  • Delivers smooth 24 fps output at HD 720p resolution for social media and content creation platforms
  • Offers practical real-time local inference workflow via a user-friendly Gradio interface

API Prising

  • 480p: $0.042;
  • 580p: $0.063;
  • 720p: $0.084

Use Cases

  • Creating animated videos from static character images for social media or digital content
  • Generating realistic motion and expression transfers for avatars and virtual characters
  • AI-powered character replacement in existing videos with controllable motion fidelity
  • Rapid prototyping and iteration of animations with local GPU inference
  • Enabling content creators and animators with minimal manual animation skills

Code Sample

Comparison with Other Models

vs FLUX.1 Kontext [dev]: Wan 2.2 offers deep motion transfer with causal temporal modeling, which excels in identity preservation and natural flow, while FLUX.1 Kontext [dev] focuses more on open-weight consistency control tailored for custom animation pipelines.

vs Adobe Animate: Wan 2.2's strength lies in AI-powered spontaneous animation from live motion data, specifically for character faces and bodies, versus Adobe Animate’s traditional frame-by-frame and vector animation tools that rely heavily on manual design input.

vs FLUX.1 Kontext Max: Wan 2.2 focuses on high-quality 720p video generation with smooth motion transfer for compact video clips, whereas FLUX.1 Kontext Max targets enterprise-grade precision and complex long animated sequences often needed in studio productions.

vs Animaker: Wan 2.2 is more technically advanced with AI-driven pose and expression transfer generating full dynamic video from a single image, while Animaker targets beginners with template-based drag-and-drop animation and limited motion customization.

API Integration

Accessible via AI/ML API. Documentation: available here.

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