arXiv cs.AI· Baiyu Chen, Zechen Li, Wilson Wongso, Lihuan Li, Xiachong Lin, Hao Xue, Benjamin Tag, Flora Salim·· 4 小时前AI 评分46
AnyMo：面向真实场景的几何感知、与佩戴设置无关的人体运动建模
AnyMo: Geometry-Aware Setup-Agnostic Modeling of Human Motion in the Wild
AI 导读
AnyMo 是一个几何感知的运动建模框架，通过基于物理的 IMU 仿真在人体表面密集位置生成合成信号，预训练图编码器，并将多位置 IMU 信号转化为全身运动 token，与 LLM 对齐实现运动-语言理解。
正文 · 原文
Abstract:As wearable and mobile devices become increasingly embedded in daily life, they offer a practical way to continuously sense human motion in the wild. But inertial signals are highly dependent on the sensing setup, including body location, mounting position, sensor orientation, device hardware, and sampling protocol. This setup dependence makes it difficult to learn motion representations that transfer across devices and datasets, and limits the broader use of wearable IMUs beyond closed-set recognition. We introduce AnyMo, a geometry-aware framework for setup-agnostic human motion modeling. AnyMo uses physics-grounded IMU simulation over dense body-surface placements to generate diverse and plausible synthetic signals, pre-trains a graph encoder from paired synthetic placement views and masked partial observations, tokenizes multi-position IMU into full-body motion tokens, and aligns these tokens with an LLM for motion-language understanding. We evaluate AnyMo on three complementary tasks: zero-shot activity recognition across 14 unseen downstream datasets, cross-modal retrieval, and wearable IMU motion captioning, where it improves average Accuracy/F1/R@2 by 11.7\%/11.6\%/22.6\% on HAR, increases zero-shot IMU-to-text and text-to-IMU retrieval MRR by 15.9\% and 28.6\%, respectively, and improves zero-shot captioning BERT-F1 by 7.5\%. These results support AnyMo as a generalist model for wearable motion understanding in the wild. Project page: this https URL.
| Comments: | NeurIPS 2026. Check out our project page for code, model, benchmark and more: this https URL |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Human-Computer Interaction (cs.HC) |
| Cite as: | arXiv:2605.22715 [cs.CV] |
| (or arXiv:2605.22715v3 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2605.22715 arXiv-issued DOI via DataCite |
Submission history
From: Baiyu Chen [view email]
[v1]
Thu, 21 May 2026 16:52:10 UTC (7,797 KB)
[v2]
Sat, 23 May 2026 13:34:44 UTC (9,463 KB)
[v3]
Tue, 10 Nov 2026 12:25:40 UTC (8,083 KB)
来源：arXiv cs.AI · arxiv.org