Motion tokenization is a key component of generalizable motion models, yet most existing approaches are restricted to species-specific skeletons, limiting their applicability across diverse morphologies. We propose NECromancer (NEC), a universal motion tokenizer that operates directly on arbitrary BVH skeletons. NEC consists of three components: (1) an Ontology-aware Skeletal Graph Encoder (OwO) that encodes structural priors from BVH files, including joint semantics, rest-pose offsets, and skeletal topology, into skeletal embeddings; (2) a Topology-Agnostic Tokenizer (TAT) that compresses motion sequences into a universal, topology-invariant discrete representation; and (3) the Unified BVH Universe (UvU), a large-scale dataset aggregating BVH motions across heterogeneous skeletons. Experiments show that NEC achieves high-fidelity reconstruction under substantial compression and effectively disentangles motion from skeletal structure. The resulting token space supports cross-species motion transfer, composition, denoising, generation with token-based models, and text-motion retrieval, establishing a unified framework for motion analysis and synthesis across diverse morphologies. Demo page: https://animotionlab.github.io/NECromancer/

翻译：运动标记化是通用运动模型的关键组成部分，然而现有方法大多局限于特定物种的骨骼结构，限制了其在多样化形态上的适用性。我们提出NECromancer（NEC），一种可直接处理任意BVH骨骼的通用运动标记器。NEC包含三个核心组件：（1）本体感知骨骼图编码器（OwO），从BVH文件中提取结构先验信息（包括关节语义、静止姿态偏移和骨骼拓扑结构）并编码为骨骼嵌入；（2）拓扑无关标记器（TAT），将运动序列压缩为通用且拓扑不变的离散表示；（3）统一BVH宇宙（UvU），一个整合异构骨骼BVH运动的大规模数据集。实验表明，NEC在高度压缩下仍能实现高保真重建，并有效解耦运动与骨骼结构。由此构建的标记空间支持跨物种运动迁移、组合、去噪、基于标记模型的运动生成以及文本-运动检索，为跨形态运动分析与合成建立了统一框架。演示页面：https://animotionlab.github.io/NECromancer/