NECromancer: Breathing Life into Skeletons via BVH Animation

Xu Mingxi, Qi WANG, Zhengyu Wen, Phong Dao Thien, Zhengyu Li, Zhang Ning, He Xiaoyu, Wei Zhao, Kehong Gong, Mingyuan Zhang

Keywords: Motion tokenization，Motion Generation，BVH Animation，Skeletal-Invariant Representation，Cross-Species Motion Transfer，Motion Compression

Abstract:

Motion tokenization is fundamental to the development of generalizable motion models, yet existing approaches remain restricted to species-specific skeletons, such as humans, thereby limiting their applicability across diverse morphologies. We present NECromancer (NEC), a universal motion tokenizer designed to operate on arbitrary BVH skeletons. NEC is built upon three core components: (1) an
Ontology-aWare Skeletal Graph EncOder (OwO), which leverages graph neural networks to encode structural priors extracted from BVH files—including joint-name semantics, rest-pose offsets, and skeletal topology—into robust skeletal embeddings; (2) a Topology-Agnostic Tokenizer (TAT), which compresses motion sequences into a universal, topology–invariant latent representation, thereby decoupling motion dynamics from morphology; and (3) the Unified BVH Universe (UvU), a large-scale dataset that consolidates BVH motions across heterogeneous skeletons (humans, quadrupeds, and other species), enabling systematic training and evaluation under diverse morphologies. Experimental results demonstrate that NEC achieves high-fidelity motion reconstruction with substantial compression, while effectively disentangling motion from skeletal structure. This capability supports a broad range of downstream tasks, including cross-species motion transfer, motion composition, denoising, generation (plug-and-play with any token-based generator; e.g., MoMask) and motion–text retrieval (via an OwO-based CLIP variant). By grounding motion representation in BVH animation while removing species-specific constraints, NEC establishes a principled framework for universal motion analysis and synthesis across varied morphologies.

Primary Area: generative models

Submission Number: 3035