Multimodal large language models (MLLMs) exhibit strong visual-language reasoning, yet remain confined to their native modalities and cannot directly process structured, non-visual data such as human skeletons. Existing methods either compress skeleton dynamics into lossy feature vectors for text alignment, or quantize motion into discrete tokens that generalize poorly across heterogeneous skeleton formats. We present SkeletonLLM, which achieves universal skeleton understanding by translating arbitrary skeleton sequences into the MLLM's native visual modality. At its core is DrAction, a differentiable, format-agnostic renderer that converts skeletal kinematics into compact image sequences. Because the pipeline is end-to-end differentiable, MLLM gradients can directly guide the rendering to produce task-informative visual tokens. To further enhance reasoning capabilities, we introduce a cooperative training strategy: Causal Reasoning Distillation transfers structured, step-by-step reasoning from a teacher model, while Discriminative Finetuning sharpens decision boundaries between confusable actions. SkeletonLLM demonstrates strong generalization on diverse tasks including recognition, captioning, reasoning, and cross-format transfer -- suggesting a viable path for applying MLLMs to non-native modalities. Code will be released upon acceptance.

翻译：多模态大语言模型（MLLMs）展现出强大的视觉-语言推理能力，但目前仍局限于其原生模态，无法直接处理人体骨骼等结构化非视觉数据。现有方法要么将骨骼动态压缩为有损特征向量以实现文本对齐，要么将运动量化为离散标记，但后者在异质骨骼格式间泛化能力较弱。我们提出SkeletonLLM，通过将任意骨骼序列转化为MLLM原生视觉模态，实现通用骨骼理解。其核心是DrAction——一种可微分、格式无关的渲染器，可将骨骼运动学转换为紧凑图像序列。由于整个流水线端到端可微分，MLLM梯度可直接指导渲染生成包含任务信息的视觉标记。为进一步增强推理能力，我们引入协同训练策略：因果推理蒸馏从教师模型迁移结构化逐步推理能力，判别式微调强化易混淆动作间的决策边界。SkeletonLLM在识别、描述生成、推理及跨格式迁移等多样化任务上展现出强泛化性，为将MLLM应用于非原生模态提供了可行路径。代码将在录用后开源。