SAMoR: 任意骨架和拓扑结构的铰接物体运动建模
SAMoR: Motion Modelling for Articulated Objects of Any Skeleton and Topology
AI总结：
提出SAMoR，一种跨拓扑的运动表示方法，将运动片段编码为固定数量的部件令牌，实现任意骨架的精确重建和跨拓扑运动迁移。
摘要：
对任意骨架拓扑的铰接物体进行运动建模仍然困难：现有的运动生成器针对固定的人体骨架，而先前的适应方法要么无法在不同骨架之间共享词汇，要么通过全局池化丢弃运动细节。我们的关键观察是，虽然关节级别的运动在不同物种间不能清晰对应，但功能性关节组的运动可以：人类手臂、狼前腿和鸟翅膀尽管关节数量和连接方式不同，但共享运动结构，这种对应关系在拓扑不匹配时也能通过关节名称（例如“前臂”、“wing_L1”）部分暴露。我们提出了SAMoR（Skeleton-Aware Motion Representation for Articulated Objects），一种跨拓扑的运动表示方法，将每个运动片段编码为固定数量（$K=8$）的部件令牌，这些令牌在任意骨架间共享。一个图变换器编码器处理每个关节的运动特征、运动学图结构和关节名称嵌入，然后通过交叉注意力池化和残差向量量化将其压缩为部件级别的令牌，形成一个跨骨架共享的离散运动码本。为了防止部件查询退化为冗余的全局表示，我们引入了一种拓扑无关的注意力监督损失，并采用关节名称丢弃以减少对文本标签的过度依赖。我们从HumanML3D、Truebones Zoo和动画Objaverse-XL资产中整理了一个异构语料库，并在具有未见骨架的保留角色上评估SAMoR。它支持精确重建和跨拓扑迁移，并通过MaskGIT令牌生成器实现文本条件生成和部件级编辑。SAMoR在跨拓扑重建上达到了$2.75 \times 10^{-2}$归一化MPJPE，比最强的自适应可变$J$令牌化基线低$5.8$倍，同时在HumanML3D上与固定骨架专家保持竞争力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
