arXiv:2603.22282v2 Announce Type: replace-cross Abstract: We present UniMotion, to our knowledge the first unified framework for simultaneous understanding and generation of human motion, natural language, and RGB images within a single architecture. Existing unified models handle only restricted modality subsets (e.g., Motion-Text or static Pose-Image) and predominantly rely on discrete tokenization, which introduces quantization errors and disrupts temporal continuity. UniMotion overcomes both limitations through a core principle: treating motion as a first-class continuous modality on equal
Advances in multi-modal AI architectures and continuous representation learning are converging, enabling more robust unified frameworks.
A truly unified framework for motion, text, and vision understanding and generation could unlock significant breakthroughs in AI's ability to interact with and interpret the physical world.
The ability to simultaneously process and generate human motion, language, and images within a single architecture moves AI closer to human-like perception and interaction, reducing modality-specific silos.
- · AI research institutions
- · Robotics companies
- · Generative AI platforms
- · Virtual/Augmented Reality developers
- · Companies dependent on siloed modality-specific AI models
- · Proprietary single-modality data providers
Improved human-robot interaction and more fluid generative AI experiences become possible.
Reduced complexity in developing multi-modal AI systems, leading to faster prototyping and deployment in various applications.
Accelerated development of general-purpose AI systems that can reason and act across diverse sensory inputs and outputs, blurring the lines between digital and physical.
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Read at arXiv cs.AI