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🔥Motion Latent Consistency Model🔥
🔥Motion Latent Consistency Model🔥
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Introducing MotionLCM💃, controlling and generating a motion in milliseconds! 
Introducing MotionLCM💃, controlling and generating a motion in milliseconds! 
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Huggingface Space: 
Huggingface Space: 
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https://huggingface.co/spaces/wxDai/MotionLCM
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wxDai/MotionLCM
https://huggingface.co/spaces/wxDai/MotionLCM
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Huggingface Paper: 
Huggingface Paper: 
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https://huggingface.co/papers/2404.19759
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2404.19759
https://huggingface.co/papers/2404.19759
MotionLCM: Real-time Controllable Motion Generation via Latent
  Consistency Model (2404.19759)
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Project page: 
Project page: 
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https://dai-wenxun.github.io/MotionLCM-page/
https://dai-wenxun.github.io/MotionLCM-page/
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Paper: 
Paper: 
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https://arxiv.org/pdf/2404.19759.pdf
https://arxiv.org/pdf/2404.19759.pdf
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Code: 
Code: 
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https://github.com/Dai-Wenxun/MotionLCM
https://github.com/Dai-Wenxun/MotionLCM
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video: 
video: 
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https://www.youtube.com/watch?v=BhrGmJYaRE4
https://www.youtube.com/watch?v=BhrGmJYaRE4
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MotionLCM supports inference pipelines of 1-4 steps, with almost no difference in effectiveness between 1 and 4 steps. Generating approximately 200 frames of motion only takes about 30ms, which averages to approximately 6k fps per frame.
MotionLCM supports inference pipelines of 1-4 steps, with almost no difference in effectiveness between 1 and 4 steps. Generating approximately 200 frames of motion only takes about 30ms, which averages to approximately 6k fps per frame.
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Our MotionLCM can achieve high-quality text-to-motion and precise motion control results (both sparse and dense conditions) in ∼30 ms.
Our MotionLCM can achieve high-quality text-to-motion and precise motion control results (both sparse and dense conditions) in ∼30 ms.
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We integrated a control module into the diffusion of the latent space, named Motion ControlNet, to achieve controllable motion generation. Our control algorithm is approximately 1,000 times faster than the best-performing baseline, with comparable quality.
We integrated a control module into the diffusion of the latent space, named Motion ControlNet, to achieve controllable motion generation. Our control algorithm is approximately 1,000 times faster than the best-performing baseline, with comparable quality.
🔥Motion Latent Consistency Model🔥

Introducing MotionLCM💃, controlling and generating a motion in milliseconds! 

Huggingface Space: 
https://huggingface.co/spaces/wxDai/MotionLCM
Huggingface Paper: 
https://huggingface.co/papers/2404.19759

Project page: https://dai-wenxun.github.io/MotionLCM-page/
Paper: https://arxiv.org/pdf/2404.19759.pdf
Code: https://github.com/Dai-Wenxun/MotionLCM
video: https://www.youtube.com/watch?v=BhrGmJYaRE4

MotionLCM supports inference pipelines of 1-4 steps, with almost no difference in effectiveness between 1 and 4 steps. Generating approximately 200 frames of motion only takes about 30ms, which averages to approximately 6k fps per frame.

Our MotionLCM can achieve high-quality text-to-motion and precise motion control results (both sparse and dense conditions) in ∼30 ms.

We integrated a control module into the diffusion of the latent space, named Motion ControlNet, to achieve controllable motion generation. Our control algorithm is approximately 1,000 times faster than the best-performing baseline, with comparable quality.
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