MagicAnimate

MagicAnimate: Temporally Consistent Human Image Animation using Diffusion Model

MagicAnimate is an open-source project that produces animated videos from a single image and a motion video using a diffusion-based framework. It focuses on maintaining temporal consistency and preserving the reference image while enhancing animation fidelity. This tool is developed by Show Lab, National University of Singapore & Bytedance.

What is MagicAnimate?

MagicAnimate excels in animating reference images with motion sequences from various sources, including cross-ID animations and unseen domains like oil paintings and movie characters. It integrates with text-to-image diffusion models like DALLE3, bringing text-prompted images to life with dynamic actions.

Key Features and Advantages:

• Temporal Consistency: Maintains consistency throughout the animation.
• Reference Image Preservation: Faithfully preserves the details of the reference image.
• Enhanced Animation Fidelity: Improves the quality and realism of the animation.
• Versatile Motion Sources: Supports motion sequences from various sources, including unseen domains.
• Integration with T2I Models: Compatible with text-to-image diffusion models like DALLE3.

Disadvantages:

• Some distortion in the face and hands.
• Style shifts from anime to realism in the faces.
• Body proportion changes when applying anime style to real human-based videos.

Getting Started with MagicAnimate:

1. Prerequisites: Ensure you have Python >= 3.8, CUDA >= 11.3, and FFmpeg installed.
2. Installation:

   conda env create -f environment.yml
   conda activate manimate
   

How to Use MagicAnimate:

1. Online Demo: Try the MagicAnimate online demo on Hugging Face or Replicate.

2. Colab: Run MagicAnimate on Google Colab using this tutorial: How to Run MagicAnimate on Colab.

3. Replicate API: Use the Replicate API to generate animated videos.

   import Replicate from "replicate";
   
   const replicate = new Replicate({
     auth: process.env.REPLICATE_API_TOKEN,
   });
   
   const output = await replicate.run(
     "lucataco/magic-animate:e24ad72cc67dd2a365b5b909aca70371bba62b685019f4e96317e59d4ace6714",
     {
       input: {
         image: "https://example.com/image.png",
         video: "Input motion video",
         num_inference_steps: 25, // Number of denoising steps
         guidance_scale: 7.5, // Scale for classifier-free guidance
         seed: 349324 // Random seed. Leave blank to randomize the seed
       }
     }
   );
   

How to Generate Motion Videos:

• Use OpenPose, a real-time multi-person keypoint detection library, to convert videos into motion videos.
• Convert a motion video to OpenPose with this model: video to openpose.
• Use the magic-animate-openpose model with OpenPose.

Additional Resources:

• Official MagicAnimate introduction
• MagicAnimate Paper.pdf
• MagicAnimate arXiv
• MagicAnimate GitHub Code
• MagicAnimate Demo

What is the primary function of MagicAnimate?

MagicAnimate's primary function is to generate animated videos from a single reference image and a motion video, ensuring temporal consistency and high fidelity.

How does MagicAnimate work?

MagicAnimate uses a diffusion-based framework to analyze the motion in the input video and apply it to the reference image, generating a new video that mimics the motion while preserving the visual characteristics of the reference image.