MagicAvatar: Multimodal Avatar Generation and Animation

What is MagicAvatar?

MagicAvatar is a two-stage avatar generation and animation system. Instead of attempting to generate avatars directly from input like text or video, it follows a structured approach:

Stage 1: Input to Motion
The system processes multimodal inputs—such as text descriptions, videos, or audio—and converts them into motion signals. These signals include body pose, depth information, and DensePose.

Stage 2: Motion to Avatar Video
With the motion signals prepared, the system generates a video of a human avatar based on these movements. If users provide reference images, the system can personalize the avatar to resemble a specific person.

MagicAvatar Overview

Key Features of MagicAvatar

• Multimodal Input Support
  • Text: Describe an action in words to guide avatar movement
  • Video: Provide a video, and the avatar will mimic the motion
  • Audio (Coming Soon): The avatar will soon be able to animate based on voice or sound input
• Two-Step Pipeline
  • First step: Input is converted into motion signals
  • Second step: Avatar video is created from these signals
• Custom Avatars
  • Users can supply images of a person
  • The output avatar will visually resemble the person and follow input-guided motion
• Reusable Motion Signals
  • The generated motion data can be reused for other avatars
  • This allows animating multiple characters with the same movement pattern
• Open-Source and Research Friendly
  • Available on GitHub for developers and researchers
  • Supported by a well-documented research paper on ArXiv

Use Cases of MagicAvatar

MagicAvatar can be applied across various industries and creative fields:

• Content Creation: Create custom characters for YouTube videos or animated explainers.
• Education: Use avatars to act out textbook scenarios or language dialogues.
• Social Media: Generate short clips with personalized avatars for TikTok or Instagram.
• Gaming: Create character intros or motion demos without needing a full motion-capture setup.
• Virtual Influencers: Build digital personas that mimic your voice and body language.

How to Use MagicAvatar (Step-by-Step)?

Let's walk through how to use MagicAvatar for different scenarios:

1. Text-Guided Avatar Generation
   What You Need:
   • A descriptive text prompt
   • Optional: Reference images to create a specific person's avatar
   Steps:
   • Clone the GitHub repository
   • Prepare your environment using the instructions in the README
   • Input your text, such as: "A person jogging in a park"
   • The system will convert this to motion signals
   • If reference images are provided, the final avatar video will match the individual
   • Output: A video file of an avatar performing the described action
2. Video-Guided Avatar Generation
   What You Need:
   • A video clip showing the desired motion
   • Optional: Target avatar images
   Steps:
   • Select a source video that clearly captures body movement
   • Upload or link the video into the framework's input pipeline
   • Provide reference images of the avatar
   • The system extracts pose, depth, and DensePose from the video
   • These signals guide the generation of the final avatar clip
   Example:
   • Input video: Person dancing
   • Output: A custom avatar replicating the dance moves
3. Multimodal Avatar Animation
   What You Need:
   • A set of images of a person
   • A motion signal from either text or video input
   Steps:
   • Generate motion signals using text or video
   • Provide 2-5 frontal images of the subject
   • The system adapts the avatar's appearance
   • It synchronizes the motion to animate the personalized character
4. Audio-Guided Avatar Generation (Coming Soon)
   What to Expect:
   • Users will input audio clips—voice recordings or sounds
   • MagicAvatar will translate audio features into appropriate body movements (e.g., hand gestures, lip sync)
   • Combined with a visual avatar, it enables speaking characters

Architecture Overview

MagicAvatar separates the process of motion understanding and visual generation. Here's a breakdown of the architecture:

This approach ensures the system remains adaptable and modular.

Technical Details

• Motion Representation
  • Includes pose keypoints
  • Depth maps
  • DensePose estimations
• Backbone Models
  • Leverages pre-trained vision models
  • Utilizes language models for text understanding
• Training Dataset
  • Large-scale motion datasets
  • Comprehensive avatar datasets
• Output Format
  • Final videos in MP4 format
  • 720p or higher resolution

Performance and Output Quality

MagicAvatar produces visually consistent avatar videos, especially when supplied with high-quality reference images and clean input data.

Comparison with Other Tools

Final Thoughts

MagicAvatar is a flexible toolkit that simplifies the process of avatar generation. Its two-stage system offers better control over motion and appearance. With multiple input methods and open-source access, it offers a great starting point for developers, artists, and content creators interested in personalized animation.