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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.

TMElyralab/MuseV

MuseV: Infinite-length and High Fidelity Virtual Human Video Generation with Visual Conditioned Parallel Denoising

46NOASSERTIONPython

View on GitHub

Stars: 2,844
Forks: 305
Watchers: 2,844
Open issues: 68
Contributors: 9

Star history, last 90 days— over 5 snapshots

2026-07-24 · 2,8442026-08-06 · 2,844

Star history, last 90 days
Date	Stars
2026-07-24	2844
2026-07-25	2844
2026-07-28	2844
2026-07-30	2844
2026-08-06	2844

Today

— stars today

This week

— stars this week

This month

— stars this month

Momentum

0.0

growth rate 0.00%/day

From the README

# MuseV [English](README.md) [中文](README-zh.md)

<font size=5>MuseV: Infinite-length and High Fidelity Virtual Human Video Generation with Visual Conditioned  Parallel Denoising
</br>
Zhiqiang Xia <sup>\*</sup>,
Zhaokang Chen<sup>\*</sup>,
Bin Wu<sup>†</sup>,
Chao Li,
Kwok-Wai Hung,
Chao Zhan,
Yingjie He, 
Wenjiang Zhou
(<sup>*</sup>co-first author, <sup>†</sup>Corresponding Author, [email protected])
</font>

Lyra Lab, Tencent Music Entertainment

**[github](https://github.com/TMElyralab/MuseV)**    **[huggingface](https://huggingface.co/TMElyralab/MuseV)**  **[HuggingfaceSpace](https://huggingface.co/spaces/AnchorFake/MuseVDemo)**  **[project](https://tmelyralab.github.io/MuseV_Page/)**    **Technical report (comming soon)**


We have setup **the world simulator vision since March 2023, believing diffusion models can simulate the world**. `MuseV` was a milestone achieved around **July 2023**. Amazed by the progress of Sora, we decided to opensource `MuseV`, hopefully it will benefit the community. Next we will move on to the promising diffusion+transformer scheme.


Update: 

1. We have released <a href="https://github.com/TMElyralab/MuseTalk" style="font-size:24px; color:red;">MuseTalk</a>, a real-time high quality lip sync model, which can be applied with MuseV as a complete virtual human generation solution.
2. :new: We are thrilled to announce that [MusePose](https://github.com/TMElyralab/MusePose/) has been released. MusePose is an image-to-video generation framework for virtual human under control signal like pose. Together with MuseV and MuseTalk, we hope the community can join us and march towards the vision where a virtual human can be generated end2end with native ability of full body movement and interaction.

# Overview
`MuseV` is a diffusion-based virtual human video generation framework, which 
1. supports **infinite length** generation using a novel **Visual Conditioned Parallel Denoising scheme**.
2. checkpoint available for virtual human video generation trained on human dataset.
3. supports Image2Video, Text2Image2Video, Video2Video.
4. compatible with the **Stable Diffusion ecosystem**, including `base_model`, `lora`, `controlnet`, etc. 
5. supports multi reference image technology, including `IPAdapter`, `ReferenceOnly`, `ReferenceNet`, `IPAdapterFaceID`.
6. training codes (comming very soon).

# Important bug fixes
1. `musev_referencenet_pose`: model_name of `unet`, `ip_adapter` of Command is not correct, please use `musev_referencenet_pose` instead of `musev_referencenet`.

# News
- [03/27/2024] release `MuseV` project and trained model `musev`, `muse_referencenet`.
- [03/30/2024] add huggingface space gradio to generate video in gui

## Model
### Overview of model structure
![model_structure](./data/models/musev_structure.png)
### Parallel denoising
![parallel_denoise](./data//models/parallel_denoise.png)

## Cases
All frames were generated directly from text2video model, without any post process.
MoreCase is in **[project](https://tmelyralab.github.io/MuseV_Page/)**, including **1-2 minute video**.

<!-- # TODO: // use youtu video link? -->
Examples bellow can be accessed at `configs/tasks/example.yaml`


### Text/Image2Video

#### Human

<table class="center">
  <tr style="font-weight: bolder;text-align:center;">
        <td width="50%">image</td>
        <td width="45%">video </td>
        <td width="5%">prompt</td>
  </tr>

  <tr>
    <td>
      <img src=./data/images/yongen.jpeg width="400">
    </td>
    <td >
     <video src="https://github.com/TMElyralab/MuseV/assets/163980830/732cf1fd-25e7-494e-b462-969c9425d277" width="100" controls preload></video>
    </td>
    <td>(masterpiece, best quality, highres:1),(1boy, solo:1),(eye blinks:1.8),(head wave:1.3)
    </td>
  </tr>

  <tr>
    <td>
      <img src=./data/images/seaside4.jpeg width="400">
    </td>
    <td>
      <video src="https://github.com/TMElyralab/MuseV/assets/163980830/9b75a46c-f4e6-45ef-ad02-05729f091c8f" width="100" controls p

Excerpt of 23,043 characters

Read on GitHub

Top contributors9 shown

itechmusic
36
zhanchao019
Chao Zhan · The Chinese University of Hong Kong · Hong Kong
9
xzqjack
8
phighting
6
czk32611
3
HonestQiao
1
mahone3297
从零开始学AI
1
asdf2kr
Gyeongbo Sim · Naver Webtoon
1
eltociear
Ikko Eltociear Ashimine · Japan
1

Quality score

Would you bet a product on this? Bounded 0–100 and slow moving.

46/10046

Maintenance: 5%; How recently the default branch was pushed to
Releases: 25%; Recency and cadence of tagged releases
Community: 55%; Contributor breadth and bus factor
Issue backlog: 85%; Open issues measured against the size of the audience
Documentation: 98%; README depth, plus a homepage and topics
Licensing: 35%; How freely the code can be adopted

What to know before adopting

No commits in over a year
No tagged releases
Licence terms need review before adoption.

Details

Created: 2 years ago
Last push: 2 years ago
Last release: none
Release tag: —
Releases in the last year: 0
Default branch: main
Size: 242k KB
Owner type: Organization
Owner location: —
Discovered via: topic:video-generation
First indexed: 14 days ago
Last synced: 12 days ago

TMElyralab/MuseV

MuseV: Infinite-length and High Fidelity Virtual Human Video Generation with Visual Conditioned Parallel Denoising

46NOASSERTIONPython

View on GitHub

Stars: 2,844
Forks: 305
Watchers: 2,844
Open issues: 68
Contributors: 9

Star history, last 90 days— over 5 snapshots

2026-07-24 · 2,8442026-08-06 · 2,844

Star history, last 90 days
Date	Stars
2026-07-24	2844
2026-07-25	2844
2026-07-28	2844
2026-07-30	2844
2026-08-06	2844

Today

— stars today

This week

— stars this week

This month

— stars this month

Momentum

0.0

growth rate 0.00%/day

From the README

# MuseV [English](README.md) [中文](README-zh.md)

<font size=5>MuseV: Infinite-length and High Fidelity Virtual Human Video Generation with Visual Conditioned  Parallel Denoising
</br>
Zhiqiang Xia <sup>\*</sup>,
Zhaokang Chen<sup>\*</sup>,
Bin Wu<sup>†</sup>,
Chao Li,
Kwok-Wai Hung,
Chao Zhan,
Yingjie He, 
Wenjiang Zhou
(<sup>*</sup>co-first author, <sup>†</sup>Corresponding Author, [email protected])
</font>

Lyra Lab, Tencent Music Entertainment

**[github](https://github.com/TMElyralab/MuseV)**    **[huggingface](https://huggingface.co/TMElyralab/MuseV)**  **[HuggingfaceSpace](https://huggingface.co/spaces/AnchorFake/MuseVDemo)**  **[project](https://tmelyralab.github.io/MuseV_Page/)**    **Technical report (comming soon)**


We have setup **the world simulator vision since March 2023, believing diffusion models can simulate the world**. `MuseV` was a milestone achieved around **July 2023**. Amazed by the progress of Sora, we decided to opensource `MuseV`, hopefully it will benefit the community. Next we will move on to the promising diffusion+transformer scheme.


Update: 

1. We have released <a href="https://github.com/TMElyralab/MuseTalk" style="font-size:24px; color:red;">MuseTalk</a>, a real-time high quality lip sync model, which can be applied with MuseV as a complete virtual human generation solution.
2. :new: We are thrilled to announce that [MusePose](https://github.com/TMElyralab/MusePose/) has been released. MusePose is an image-to-video generation framework for virtual human under control signal like pose. Together with MuseV and MuseTalk, we hope the community can join us and march towards the vision where a virtual human can be generated end2end with native ability of full body movement and interaction.

# Overview
`MuseV` is a diffusion-based virtual human video generation framework, which 
1. supports **infinite length** generation using a novel **Visual Conditioned Parallel Denoising scheme**.
2. checkpoint available for virtual human video generation trained on human dataset.
3. supports Image2Video, Text2Image2Video, Video2Video.
4. compatible with the **Stable Diffusion ecosystem**, including `base_model`, `lora`, `controlnet`, etc. 
5. supports multi reference image technology, including `IPAdapter`, `ReferenceOnly`, `ReferenceNet`, `IPAdapterFaceID`.
6. training codes (comming very soon).

# Important bug fixes
1. `musev_referencenet_pose`: model_name of `unet`, `ip_adapter` of Command is not correct, please use `musev_referencenet_pose` instead of `musev_referencenet`.

# News
- [03/27/2024] release `MuseV` project and trained model `musev`, `muse_referencenet`.
- [03/30/2024] add huggingface space gradio to generate video in gui

## Model
### Overview of model structure
![model_structure](./data/models/musev_structure.png)
### Parallel denoising
![parallel_denoise](./data//models/parallel_denoise.png)

## Cases
All frames were generated directly from text2video model, without any post process.
MoreCase is in **[project](https://tmelyralab.github.io/MuseV_Page/)**, including **1-2 minute video**.

<!-- # TODO: // use youtu video link? -->
Examples bellow can be accessed at `configs/tasks/example.yaml`


### Text/Image2Video

#### Human

<table class="center">
  <tr style="font-weight: bolder;text-align:center;">
        <td width="50%">image</td>
        <td width="45%">video </td>
        <td width="5%">prompt</td>
  </tr>

  <tr>
    <td>
      <img src=./data/images/yongen.jpeg width="400">
    </td>
    <td >
     <video src="https://github.com/TMElyralab/MuseV/assets/163980830/732cf1fd-25e7-494e-b462-969c9425d277" width="100" controls preload></video>
    </td>
    <td>(masterpiece, best quality, highres:1),(1boy, solo:1),(eye blinks:1.8),(head wave:1.3)
    </td>
  </tr>

  <tr>
    <td>
      <img src=./data/images/seaside4.jpeg width="400">
    </td>
    <td>
      <video src="https://github.com/TMElyralab/MuseV/assets/163980830/9b75a46c-f4e6-45ef-ad02-05729f091c8f" width="100" controls p

Excerpt of 23,043 characters

Read on GitHub

Top contributors9 shown

itechmusic
36
zhanchao019
Chao Zhan · The Chinese University of Hong Kong · Hong Kong
9
xzqjack
8
phighting
6
czk32611
3
HonestQiao
1
mahone3297
从零开始学AI
1
asdf2kr
Gyeongbo Sim · Naver Webtoon
1
eltociear
Ikko Eltociear Ashimine · Japan
1

Quality score

Would you bet a product on this? Bounded 0–100 and slow moving.

46/10046

Maintenance: 5%; How recently the default branch was pushed to
Releases: 25%; Recency and cadence of tagged releases
Community: 55%; Contributor breadth and bus factor
Issue backlog: 85%; Open issues measured against the size of the audience
Documentation: 98%; README depth, plus a homepage and topics
Licensing: 35%; How freely the code can be adopted

What to know before adopting

No commits in over a year
No tagged releases
Licence terms need review before adoption.

Details

Created: 2 years ago
Last push: 2 years ago
Last release: none
Release tag: —
Releases in the last year: 0
Default branch: main
Size: 242k KB
Owner type: Organization
Owner location: —
Discovered via: topic:video-generation
First indexed: 14 days ago
Last synced: 12 days ago

Top AI Repos — open-source AI, indexed and scored

TMElyralab/MuseV

From the README

Top contributors9 shown

Quality score

What to know before adopting

Categories

Details

TMElyralab/MuseV

From the README

Top contributors9 shown

Quality score

What to know before adopting

Categories

Details

Top AI Repos — open-source AI, indexed and scored

Trend

From the README

Top contributors9 shown

Quality score

What to know before adopting

Categories

Details

Trend

From the README

Top contributors9 shown

Quality score

What to know before adopting

Categories

Details