UniMotion: A Unified Framework for Motion-Text-Vision Understanding and Generation

• URL: http://arxiv.org/abs/2603.22282v1
• Date: Mon, 23 Mar 2026 17:59:48 GMT
• Title: UniMotion: A Unified Framework for Motion-Text-Vision Understanding and Generation
• Abstract summary: UniMotion is a framework for simultaneous understanding and generation of human motion, natural language, and RGB images.<n>It treats motion as a first-class continuous modality on equal footing with RGB.<n>UniMotion achieves state-of-the-art performance across seven tasks spanning any-to-any understanding, generation, and editing.
• Score: 47.383717641500844
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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 footing with RGB. A novel Cross-Modal Aligned Motion VAE (CMA-VAE) and symmetric dual-path embedders construct parallel continuous pathways for Motion and RGB within a shared LLM backbone. To inject visual-semantic priors into motion representations without requiring images at inference, we propose Dual-Posterior KL Alignment (DPA), which distills a vision-fused encoder's richer posterior into the motion-only encoder. To address the cold-start problem -- where text supervision alone is too sparse to calibrate the newly introduced motion pathway -- we further propose Latent Reconstruction Alignment (LRA), a self-supervised pre-training strategy that uses dense motion latents as unambiguous conditions to co-calibrate the embedder, backbone, and flow head, establishing a stable motion-aware foundation for all downstream tasks. UniMotion achieves state-of-the-art performance across seven tasks spanning any-to-any understanding, generation, and editing among the three modalities, with especially strong advantages on cross-modal compositional tasks.

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