arXiv:2605.29488v1 Announce Type: cross Abstract: Conditional human motion generation remains a fundamental challenge in computer vision and robotics. Despite significant progress, current methods are often constrained by fixed modality configurations and task-specific architectures, leaving cross-modal interactions and the scaling laws of multimodal-conditioned synthesis largely underexplored. A key bottleneck is the scarcity of large-scale modality-aligned motion data, limiting generalization across diverse control signals. In this work, we introduce OmniHuMo, a large-scale, high-quality dat
The continuous advancements in AI and specifically in large-scale model training are pushing the boundaries of multimodal generation, making this development timely.
This development addresses a critical bottleneck in computer vision and robotics by enabling more versatile human motion generation, crucial for advanced AI agents and humanoid platforms.
The ability to generate human motion from any modality using a unified architecture and large-scale data removes previous constraints of fixed configurations and task-specific designs.
- · Robotics companies
- · AI research institutions
- · Gaming and animation studios
- · Developers of AI agents
- · Companies reliant on highly specialized, single-modality motion capture systems
- · Traditional animation techniques
More realistic and adaptable virtual characters and robotic movements become possible.
Accelerated development of general-purpose humanoid robots and AI agents capable of complex physical interactions.
Enhanced realism in virtual environments and potential for new forms of human-computer interaction based on generated motion.
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Read at arXiv cs.AI