MusePose
Maintainer: TMElyralab
60
⛏️
| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
MusePose is an image-to-video generation framework for virtual human characters. It can generate dance videos of a human character in a reference image under a given pose sequence. This model builds upon previous work like AnimateAnyone and Moore-AnimateAnyone, with several key improvements. The maintainers of MusePose, TMElyralab, have released the model and pretrained checkpoints, and plan to continue enhancing it with features like a "pose align" algorithm and improved model architecture.
Model inputs and outputs
Inputs
- Reference image of a human character
- Sequence of poses to drive the character's movement
Outputs
- Video of the human character in the reference image performing the specified poses
Capabilities
MusePose can generate high-quality dance videos of a virtual human character, exceeding the performance of many existing open-source models in this domain. The "pose align" algorithm allows users to align arbitrary dance videos to arbitrary reference images, significantly improving inference performance and usability.
What can I use it for?
The MusePose model can be used to create virtual dance performances, animated videos, and other applications where a human character needs to be generated and driven by a sequence of poses. This could be useful for game development, film/TV production, social media content creation, and more. By combining MusePose with other models like MuseV and MuseTalk, the community can work towards the vision of generating fully animated, interactive virtual humans.
Things to try
One interesting aspect of MusePose is the ability to align arbitrary dance videos to reference images. This could allow for creative mixing and matching of different dance styles and character models. Additionally, exploring the limits of the model's pose generation capabilities, such as more complex or dynamic movements, could lead to new and compelling virtual human animations.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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