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Interested in upgrading your Motion Matching system? Check out our #SIGGRAPH2024 work where we introduce *Codebook Matching* to train neural character controllers for synthesizing lifelike embodied animations in #Games and #VR! Project Page:

18,659 просмотров • 2 лет назад •via X (Twitter)

Комментарии: 4

Фото профиля Paul Starke
Paul Starke2 лет назад

Paper can be found here

Фото профиля Zhiyang (Frank) Dou
Zhiyang (Frank) Dou2 лет назад

Cool work!

Фото профиля Michael Gold
Michael Gold2 лет назад

Awesome work. Congrats!

Фото профиля Andrew Carr (e/🤸)
Andrew Carr (e/🤸)2 лет назад

Very cool work!

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Physics-based Motion Retargeting from Sparse Inputs paper page: Avatars are important to create interactive and immersive experiences in virtual worlds. One challenge in animating these characters to mimic a user's motion is that commercial AR/VR products consist only of a headset and controllers, providing very limited sensor data of the user's pose. Another challenge is that an avatar might have a different skeleton structure than a human and the mapping between them is unclear. In this work we address both of these challenges. We introduce a method to retarget motions in real-time from sparse human sensor data to characters of various morphologies. Our method uses reinforcement learning to train a policy to control characters in a physics simulator. We only require human motion capture data for training, without relying on artist-generated animations for each avatar. This allows us to use large motion capture datasets to train general policies that can track unseen users from real and sparse data in real-time. We demonstrate the feasibility of our approach on three characters with different skeleton structure: a dinosaur, a mouse-like creature and a human. We show that the avatar poses often match the user surprisingly well, despite having no sensor information of the lower body available. We discuss and ablate the important components in our framework, specifically the kinematic retargeting step, the imitation, contact and action reward as well as our asymmetric actor-critic observations. We further explore the robustness of our method in a variety of settings including unbalancing, dancing and sports motions.

AK

106,527 просмотров • 3 лет назад