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const main = async () => {
const response = await fetch('https://api.ai.cc/v2/video/generations', {
method: 'POST',
headers: {
Authorization: 'Bearer ',
'Content-Type': 'application/json',
},
body: JSON.stringify({
model: 'alibaba/wan2.2-14b-animate-move',
prompt: 'Mona Lisa puts on glasses with her hands.',
video_url: 'https://storage.googleapis.com/falserverless/example_inputs/wan_animate_input_video.mp4',
image_url: 'https://s2-111386.kwimgs.com/bs2/mmu-aiplatform-temp/kling/20240620/1.jpeg',
resolution: "720p",
}),
}).then((res) => res.json());
console.log('Generation:', response);
};
main()
import requests
def main():
url = "https://api.ai.cc/v2/video/generations"
payload = {
"model": "alibaba/wan2.2-14b-animate-move",
"prompt": "Mona Lisa puts on glasses with her hands.",
"video_url": "https://storage.googleapis.com/falserverless/example_inputs/wan_animate_input_video.mp4",
"image_url": "https://s2-111386.kwimgs.com/bs2/mmu-aiplatform-temp/kling/20240620/1.jpeg",
"resolution": "720p",
}
headers = {"Authorization": "Bearer ", "Content-Type": "application/json"}
response = requests.post(url, json=payload, headers=headers)
print("Generation:", response.json())
if __name__ == "__main__":
main()
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Product Detail
The Wan 2.2 14B Animate Move is a cutting-edge large-scale AI video generation model engineered specifically for animating static character images with unparalleled control. It brings still photos to life by transferring intricate movements and expressions from a reference video, making it an invaluable tool for creators.
Users can effortlessly upload a static character image and a drive video containing the desired motions. The system intelligently extracts poses and masks, then animates the character. In its primary Animation mode, it creates a brand-new video where the static character precisely mimics the gestures and angles from the drive video, producing highly realistic and engaging animated content.
⚙️ Technical Specifications
- Model Size: 14 billion parameters (generation backbone)
- Architecture: Diffusion transformer model with Mixture-of-Experts (MoE) design for enhanced 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
- GPU Compatibility: Successfully tested on high-end GPUs like NVIDIA H100 (80GB) with recommended VRAM of ~75 GB for extended sequences.
- Output Quality: Capable of producing coherent, high-quality videos with natural-looking character motions and expressions.
- Identity Preservation: Demonstrates robust identity preservation from a single reference image during dynamic motion transfer.
- Environment: Optimized for Ubuntu and CUDA-enabled environments with modern PyTorch stacks.
- Content Length: Handles video lengths suitable for social media clips and short animated content effectively.
✨ Key Features
- Precise Motion Transfer: Animates static images using live motion from reference videos, transferring both body and facial expressions precisely.
- Efficient Architecture: Mixture-of-Experts architecture enables handling complex motions and detailed expression mapping without added compute cost.
- Temporal Stability: High temporal stability in motion thanks to a causal 3D compression method, preventing artifacts caused by future frame leakage.
- Realistic Integration: Supports realistic integration of animated characters with surroundings, controlling lighting and color to match backgrounds dynamically.
- High-Quality Output: Delivers smooth 24 fps output at HD 720p resolution for social media and content creation platforms.
- Real-time Inference: Offers practical real-time local inference workflow via a user-friendly Gradio interface.
💲 API Pricing
- 480p: $0.042
- 580p: $0.063
- 720p: $0.084
💡 Use Cases
- Social Media & Digital Content: Creating animated videos from static character images for engaging online presence.
- Avatar & Virtual Character Animation: Generating realistic motion and expression transfers for avatars and virtual characters in games or metaverses.
- AI-powered Character Replacement: Replacing characters in existing videos with controllable motion fidelity.
- Animation Prototyping: Rapid prototyping and iteration of animations with local GPU inference capabilities.
- Empowering Creators: Enabling content creators and animators with minimal manual animation skills to produce professional-grade animations.
🔍 Comparison with Other Models
When evaluating AI animation solutions, it's crucial to understand how Wan 2.2 14B Animate Move stands apart:
- vs FLUX.1 Kontext [dev]: Wan 2.2 offers deep motion transfer with causal temporal modeling, excelling in identity preservation and natural flow. In contrast, 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. This contrasts with Adobe Animate's traditional frame-by-frame and vector animation tools that heavily rely on manual design input.
- vs FLUX.1 Kontext Max: Wan 2.2 is optimized for high-quality 720p video generation with smooth motion transfer for compact video clips. FLUX.1 Kontext Max, however, targets enterprise-grade precision and complex long animated sequences often needed in studio productions.
- vs Animaker: Wan 2.2 is technically advanced with AI-driven pose and expression transfer, generating full dynamic video from a single image. Animaker targets beginners with template-based drag-and-drop animation and limited motion customization.
🔌 API Integration
Wan 2.2 14B Animate Move is accessible via the AI/ML API. Comprehensive documentation can be found available here.
❓ Frequently Asked Questions (FAQ)
What is Wan 2.2 14B Animate Move?
It's an advanced AI model designed to generate animated videos by transferring movements and expressions from a reference video onto a static character image. It brings still photos to life with dynamic motion.
How does it differ from traditional animation software?
Unlike traditional software requiring manual frame-by-frame or keyframe input, Wan 2.2 uses AI to automatically extract motion from live videos and apply it to a static image, significantly reducing the effort and skill needed for animation.
What kind of output quality can I expect?
The model generates high-quality 720p videos at 24 frames per second (fps) with natural-looking character motions and expressions, ensuring robust identity preservation from the original static image.
Is it suitable for professional use?
Yes, its capabilities for realistic motion transfer, high temporal stability, and HD output make it ideal for content creators, animators, and developers looking to produce professional-grade animated content for social media, virtual characters, and rapid prototyping.
What are the technical requirements for running this model?
For extended sequences, high-end GPUs like NVIDIA H100 (80GB) with approximately 75 GB VRAM are recommended. It is optimized for Ubuntu and CUDA-enabled environments using modern PyTorch stacks, offering real-time local inference via a Gradio interface.
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