MotionBricks: Scalable Real-Time Motions with Modular Latent Generative Model and Smart Primitives
Tingwu Wang, Olivier Dionne, Michael De Ruyter, David Minor, Davis Rempe, Kaifeng Zhao, Mathis Petrovich, Ye Yuan, Chenran Li, Zhengyi Luo, Brian Robison, Xavier Blackwell, Bernardo Antoniazzi, Xue Bin Peng, Yuke Zhu, Simon Yuen

TL;DR
MotionBricks introduces a scalable, real-time generative motion framework using modular latent models and smart primitives, enabling high-quality, multi-modal control for animation and robotics.
Contribution
It presents a large-scale modular latent generative model and smart primitives for real-time, high-quality motion synthesis and control, bridging research and industrial needs.
Findings
Achieves 15,000 FPS with 2ms latency in motion generation.
Models over 350,000 motion clips with a single framework.
Demonstrates application in animation and robot control.
Abstract
Despite transformative advances in generative motion synthesis, real-time interactive motion control remains dominated by traditional techniques. In this work, we identify two key challenges in bridging research and production: 1) Real-time scalability: Industry applications demand real-time generation of a vast repertoire of motion skills, while generative methods exhibit significant degradation in quality and scalability under real-time computation constraints, and 2) Integration: Industry applications demand fine-grained multi-modal control involving velocity commands, style selection, and precise keyframes, a need largely unmet by existing text- or tag-driven models. To overcome these limitations, we introduce MotionBricks: a large-scale, real-time generative framework with a two-fold solution. First, we propose a large-scale modular latent generative backbone tailored for robust…
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