NECromancer: Breathing Life into Skeletons via BVH Animation
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arXiv
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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866915779775037440 |
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| author | Xu, Mingxi Wang, Qi Wen, Zhengyu Thien, Phong Dao Li, Zhengyu Zhang, Ning He, Xiaoyu Zhao, Wei Gong, Kehong Zhang, Mingyuan |
| author_facet | Xu, Mingxi Wang, Qi Wen, Zhengyu Thien, Phong Dao Li, Zhengyu Zhang, Ning He, Xiaoyu Zhao, Wei Gong, Kehong Zhang, Mingyuan |
| contents | 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/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_06548 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | NECromancer: Breathing Life into Skeletons via BVH Animation Xu, Mingxi Wang, Qi Wen, Zhengyu Thien, Phong Dao Li, Zhengyu Zhang, Ning He, Xiaoyu Zhao, Wei Gong, Kehong Zhang, Mingyuan Computer Vision and Pattern Recognition Machine Learning 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/ |
| title | NECromancer: Breathing Life into Skeletons via BVH Animation |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2602.06548 |