SinMDM: Single Motion Diffusion

🤖✨ Meet SinMDM: Your go-to tool for creating diverse and high-quality animations of humans, animals, and creatures with unique skeletons and motions! 🦄🕺🏽🐆 Easily generate crowds, perform style transfers, and more with this efficient and innovative AI model. Check it out: https://sinmdm.github.io/SinMDM-page/ #AI #Animation #Innovation

SinMDM is a model that learns internal motion motifs from a single motion sequence and generates motions faithful to those motifs.
It specializes in synthesizing animations of humans, animals, and imaginary creatures with unique skeletons and motion patterns.
SinMDM is a lightweight architecture utilizing diffusion models and denoising networks to ensure motion diversity and avoid overfitting.
Applications of SinMDM include spatial/temporal in-betweening, motion expansion, style transfer, and crowd animation.
The model can generate crowds performing various motions based on a single input sequence, like hopping ostriches, jaguars, or breakdancing dragons.
SinMDM excels in quality and time-space efficiency compared to existing methods and does not require additional training for different applications during inference.
It leverages a shallow UNet with a QnA local attention layer to generate diverse synthesized motions while retaining core motion motifs.
SinMDM can compose motions temporally by completing missing parts or spatially by controlling selected joints, as shown with upper body control examples.
The model can handle style transfer by adjusting content motion to match learned style motions and generate long animations up to 60 seconds without further training.
The work by Raab et al. on Single Motion Diffusion is presented at the ICLR 2024 conference, showcasing their innovative approach in motion synthesis.