MotionBricks: Scalable Real-Time Motions with Modular Latent Generative Model and Smart Primitives
Authors:
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
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…
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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 real-time motion generation, effectively modeling a dataset of over 350,000 motion clips with a single model. Second, we introduce smart primitives that provide a unified, robust, and intuitive interface for authoring both navigation and object interaction. Applications can be designed in a plug-and-play manner like assembling bricks without expert animation knowledge. Quantitatively, we show that MotionBricks produces state-of-the-art motion quality on open-source and proprietary datasets of various scales, while also achieving a real-time throughput of 15,000 FPS with 2ms latency. We demonstrate the flexibility and robustness of MotionBricks in a complete production-level animation demo, covering navigation and object-scene interaction across various styles with a unified model. To showcase our framework's application beyond animation, we deploy MotionBricks on the Unitree G1 humanoid robot to demonstrate its flexibility and generalization for real-time robotic control.
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Submitted 27 April, 2026;
originally announced April 2026.
Kimodo: Scaling Controllable Human Motion Generation
Authors:
Davis Rempe,
Mathis Petrovich,
Ye Yuan,
Haotian Zhang,
Xue Bin Peng,
Yifeng Jiang,
Tingwu Wang,
Umar Iqbal,
David Minor,
Michael de Ruyter,
Jiefeng Li,
Chen Tessler,
Edy Lim,
Eugene Jeong,
Sam Wu,
Ehsan Hassani,
Michael Huang,
Jin-Bey Yu,
Chaeyeon Chung,
Lina Song,
Olivier Dionne,
Jan Kautz,
Simon Yuen,
Sanja Fidler
Abstract:
High-quality human motion data is becoming increasingly important for applications in robotics, simulation, and entertainment. Recent generative models offer a potential data source, enabling human motion synthesis through intuitive inputs like text prompts or kinematic constraints on poses. However, the small scale of public mocap datasets has limited the motion quality, control accuracy, and gen…
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High-quality human motion data is becoming increasingly important for applications in robotics, simulation, and entertainment. Recent generative models offer a potential data source, enabling human motion synthesis through intuitive inputs like text prompts or kinematic constraints on poses. However, the small scale of public mocap datasets has limited the motion quality, control accuracy, and generalization of these models. In this work, we introduce Kimodo, an expressive and controllable kinematic motion diffusion model trained on 700 hours of optical motion capture data. Our model generates high-quality motions while being easily controlled through text and a comprehensive suite of kinematic constraints including full-body keyframes, sparse joint positions/rotations, 2D waypoints, and dense 2D paths. This is enabled through a carefully designed motion representation and two-stage denoiser architecture that decomposes root and body prediction to minimize motion artifacts while allowing for flexible constraint conditioning. Experiments on the large-scale mocap dataset justify key design decisions and analyze how the scaling of dataset size and model size affect performance.
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Submitted 16 March, 2026;
originally announced March 2026.