This asset generates realistic and smooth transitions for basic humanoid locomotion.
For more details about this asset: Click Here
This is a paid asset, but now you can download the “Learned Motion Matching” for FREE. Please note that this package is provided solely for learning purposes or to test the product before purchase, and not for commercial use.
Learned Motion Matching v1.0
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Please find the Demo in the “Try it out” section of the website.
Key Features:
???? Natural, Fluid Movement: Achieve smooth, weight-shifted motion with no foot sliding or abrupt animation changes like in State Machine animations. The customizable kNN classifier balances between fast, responsive transitions and realistic movements.
???? Waypoint Navigation: With an advanced Distance Matching system, powered by a Greedy Algorithm, your characters can follow custom-defined paths with precision. Loop waypoints effortlessly and adjust the speed to control how your character interacts with the environment.
???? Upper Body Customization: Bring hand interactions to life! Using Inverse Kinematics (IK), your characters can dynamically hold, grasp, or manipulate objects with realistic hand poses. Customize hand weights, fine-tune motions, and create complex finger movements with ease.
⚙️ Effortless Setup: Designed for all humanoid characters, with a one-click setup process and comprehensive documentation to get you up and running in no time.
Dependencies:
Learned Motion Matching requires the Animation Rigging and Sentis package from the package manager.
Limitations:
Locomotion Only: The package is focused solely on basic human locomotion; advanced moves like jumping or vaulting are not included.
No Specialized Motions: Custom movements, such as limping or skipping, require external data and it is not included in this package.
Requires Sentis Version 1.3 which is an older version of Sentis.
Research:
This asset implements the SSIGRAPH technical paper <Taming Diffusion Probabilistic Models for Character Control>.
Citation:
Chen, R., Shi, M., Huang, S., Tan, P., Komura, T., & Chen, X. (2024, April 23). Taming diffusion probabilistic models for character control. arXiv.org. https://arxiv.org/abs/2404.15121
