NECromancer: Breathing Life into Skeletons via BVH Animation
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Abstract (arXiv)
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/
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 29 of its 35 references that are in the map. First posted on arXiv 6 Feb 2026.
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29 of 35 references.
References · 29
- 2017Neural Discrete Representation Learningcited by 4,712
- 2019Expressive Body Capture: 3D Hands, Face, and Body from a Single Imagecited by 1,612
- 2022Objaverse: A Universe of Annotated 3D Objectscited by 1,540
- 2018On the Continuity of Rotation Representations in Neural Networkscited by 1,214
- 2022Human Motion Diffusion Modelcited by 1,080
- 2022MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Modelcited by 780
- 2023Objaverse-XL: A Universe of 10M+ 3D Objectscited by 774
- 2023T2M-GPT: Generating Human Motion from Textual Descriptions with Discrete Representationscited by 588
- 2022Executing your Commands via Motion Diffusion in Latent Spacecited by 575
- 2023MotionGPT: Human Motion as a Foreign Languagecited by 527
- 2022TEMOS: Generating diverse human motions from textual descriptionscited by 492
- 2022MotionCLIP: Exposing Human Motion Generation to CLIP Spacecited by 408
17 more
- 2023MoMask: Generative Masked Modeling of 3D Human Motionscited by 400
- 2022TM2T: Stochastic and Tokenized Modeling for the Reciprocal Generation of 3D Human Motions and Textscited by 359
- 2016The KIT Motion-Language Datasetcited by 338
- 2023ReMoDiffuse: Retrieval-Augmented Motion Diffusion Modelcited by 287
- 2022FLAME: Free-form Language-based Motion Synthesis & Editingcited by 217
- 2022Bailando: 3D Dance Generation by Actor-Critic GPT with Choreographic Memorycited by 212
- 2023GestureDiffuCLIP: Gesture Diffusion Model with CLIP Latentscited by 158
- 2024L4GM: Large 4D Gaussian Reconstruction Modelcited by 144
- 2020Skeleton-Aware Networks for Deep Motion Retargetingcited by 124
- 2021Audio2Gestures: Generating Diverse Gestures from Speech Audio with Conditional Variational Autoencoderscited by 109
- 2024DartControl: A Diffusion-Based Autoregressive Motion Model for Real-Time Text-Driven Motion Controlcited by 84
- 2020Who Left the Dogs Out? 3D Animal Reconstruction with Expectation Maximization in the Loopcited by 81
- 2024DreamMesh4D: Video-to-4D Generation with Sparse-Controlled Gaussian-Mesh Hybrid Representationcited by 58
- 2025AnyTop: Character Animation Diffusion with Any Topologycited by 41
- 2025MotionGPT3: Human Motion as a Second Modalitycited by 37
- 2024AvatarGO: Zero-shot 4D Human-Object Interaction Generation and Animationcited by 25
- 2024Beat-It: Beat-Synchronized Multi-Condition 3D Dance Generationcited by 24
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