ARDY: Autoregressive Diffusion with Hybrid Representation for Interactive Human Motion Generation
arXiv:2607.08741v1 Announce Type: cross Abstract: Generating realistic 3D human motions in real-time within interactive applications is key for animation, simulation, and humanoid robotics. While recent offline motion generation approaches offer precise control via text and kinematic constraints, they lack the inference speed required for interactive settings. Conversely, existing online methods enable real-time synthesis but often sacrifice controllability or struggle with complex text semantics and long-horizon goals due to limited context windows. In this work, we introduce ARDY, a streamin
The continuous advancements in AI and robotics, coupled with increasing demand for realistic and interactive digital environments, make real-time human motion generation a critical current challenge.
This development can significantly accelerate the quality and adoption of humanoid robots and interactive digital experiences by enabling more natural and responsive motion, reducing development time and cost.
The ability to generate realistic and controllable 3D human motions in real-time addresses a key bottleneck in animation, simulation, and humanoid robotics, potentially democratizing advanced motion control.
- · Humanoid robotics manufacturers
- · Gaming and VR/AR developers
- · Animation studios
- · Simulation and training companies
- · Manual motion capture specialists
- · Companies reliant on pre-rendered motion libraries
Improved human-robot interaction and realistic virtual character behavior across various applications.
Faster development cycles for embodied AI systems and more immersive digital content creation.
Enhanced human-like capabilities in general-purpose humanoid robots, driving broader adoption in labor and service sectors.
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