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.

翻译：我们提出UniMotion，据我们所知，这是首个在同一架构内同时实现人体运动、自然语言与RGB图像理解与生成的统一框架。现有统一模型仅能处理受限的模态子集（如运动-文本或静态姿态-图像），且主要依赖离散标记化方法，这引入了量化误差并破坏了时间连续性。UniMotion通过核心原则克服了这两大局限：将运动视为与RGB对等的第一类连续模态。新颖的跨模态对齐运动变分自编码器及对称双路径嵌入器在共享大语言模型主干中为运动与RGB构建了并行连续路径。为在无需推理阶段输入图像的前提下向运动表示注入视觉语义先验，我们提出双后验KL对齐，将视觉融合编码器的更丰富后验知识蒸馏至仅运动编码器。针对冷启动问题——即纯文本监督过于稀疏而无法校准新引入的运动路径——我们进一步提出潜空间重建对齐，这是一种自监督预训练策略，通过密集运动潜变量作为无歧义条件来协同校准嵌入器、主干网络与流预测头，为所有下游任务建立稳定的运动感知基础。UniMotion在涵盖三种模态间任意-任意理解、生成与编辑的七项任务中达到最优性能，尤其在跨模态组合任务上展现出显著优势。