SAMoR: Motion Modelling for Articulated Objects of Any Skeleton and Topology

2026-07-02

AI Summary

Existing methods struggle to uniformly model joint motion across arbitrary skeleton topologies, often constrained by fixed architectures or suffering semantic distortion during cross-skeleton transfer. This work proposes a skeleton-aware motion representation that jointly encodes joint motion, bone connectivity, and semantic joint names via a graph Transformer. It introduces a functional joint group correspondence mechanism, a topology-agnostic attention supervision loss, and a joint name dropout strategy to achieve cross-skeleton action semantic alignment. By integrating cross-attention pooling, residual vector quantization, and a MaskGIT generative model, the approach constructs a part-level discrete motion codebook, enabling high-quality motion transfer, text-guided generation, and fine-grained editing. On cross-topology reconstruction tasks, it achieves a normalized MPJPE of 2.75×10⁻², reducing error by 5.8× compared to the strongest baseline.

Abstract

Modeling motion for articulated objects of arbitrary skeleton topology remains difficult: existing motion generators target a fixed human skeleton, and prior adaptations either fail to share a vocabulary across rigs or discard motion detail through global pooling. Our key observation is that while joint-level motion does not correspond cleanly across species, motion of functional joint groups does: a human arm, a wolf foreleg, and a bird wing share motion structure despite differing joint counts and connectivity, a correspondence that joint names (e.g., "forearm", "wing_L1") partially expose even when topology does not. We introduce SAMoR (Skeleton-Aware Motion Representation for Articulated Objects), a cross-topology motion representation that encodes each motion segment as a small fixed number ($K=8$) of part tokens shared across arbitrary skeletons. A graph-transformer encoder consumes per-joint motion features, kinematic graph structure, and joint-name embeddings, then compresses them into part-level tokens via cross-attention pooling and residual vector quantization, yielding a discrete motion codebook shared across rigs. To keep the part queries from collapsing into redundant global representations, we introduce a topology-agnostic attention supervision loss, with joint-name dropout to reduce over-reliance on text labels. We curate a heterogeneous corpus from HumanML3D, Truebones Zoo, and animated Objaverse-XL assets, and evaluate SAMoR on held-out characters with unseen skeletons. It supports accurate reconstruction and cross-topology transfer, and enables text-conditioned generation and part-wise editing via a MaskGIT token generator. SAMoR reaches $2.75 \times 10^{-2}$ normalized MPJPE on cross-topology reconstruction, $5.8\times$ below the strongest adapted variable-$J$ tokenizer baseline, while remaining competitive with fixed-skeleton specialists on HumanML3D.

Problem

• articulated objects
• skeleton topology
• motion modeling
• cross-topology representation
• motion generation

Innovation

• cross-topology motion representation
• skeleton-agnostic modeling
• part-level tokenization
• graph-transformer encoder
• discrete motion codebook