KinMo: Kinematic-aware Human Motion Understanding and Generation

ICCV 2025

Pengfei Zhang1*
Pinxin Liu2*
Pablo Garrido4
Hyeongwoo Kim3
Bindita Chaudhuri4†
*equal contribution

1University of California, Irvine   2University of Rochester   3Imperial College, London   4Flawless AI
Paper arXiv Dataset

We present KinMo, a method designed for fine-grained motion understanding for (a) efficient motion retrieval based on text descriptions, text-aligned motion (b) generation and (c) editing, and (d) trajectory control on local kinematic body parts.

Abstract

Current human motion synthesis frameworks rely on global action descriptions, creating a modality gap that limits both motion understanding and generation capabilities. A single coarse description, such as run, fails to capture details such as variations in speed, limb positioning, and kinematic dynamics, leading to ambiguities between text and motion modalities. To address this challenge, we introduce KinMo, a unified framework built on a hierarchical describable motion representation that extends beyond global actions by incorporating kinematic group movements and their interactions. We design an automated annotation pipeline to generate high-quality, fine-grained descriptions for this decomposition, resulting in the KinMo dataset and offering a scalable and cost-efficient solution for dataset enrichment. To leverage these structured descriptions, we propose Hierarchical Text-Motion Alignment that progressively integrates additional motion details, thereby improving semantic motion understanding. Furthermore, we introduce a coarse-to-fine motion generation procedure to leverage enhanced spatial understanding to improve motion synthesis. Experimental results show that KinMo significantly improves motion understanding, demonstrated by enhanced text-motion retrieval performance and enabling more fine-grained motion generation and editing capabilities.

Method

Overview of KinMo. The pipeline steps include three components: (1) semi-supervised dataset annotation with LLMs; (2) hierarchical text-motion alignment for fine-grained motion understanding; (3) coarse-to-fine motion generation for downstream applications.

Demo Video

Motion Generation Comparisons

A person holds their arms up as if holding a dance partner's hand and back, then dances in a square pattern.

A person who is standing with his hands by his sides, crosses his arms, then slightly adjusts his arms in the crossed position, then drops his arms to his original position.

A man leans forward to pick up an object slightly to his right, and places it down slightly to his left.

A man is dancing, jumping from side to side waving his arms above his head.

A man stretches his arms out to the side shoulder length, then brings them in front of him with elbow slightly bent.

A person looks for something on the ground with right hand.

We compare our method with MMM, MoMask and STMC. Our method achieves natural movements closely aligned with the text descriptions, even with long and complex text descriptions. The static texture on the meshes is randomly chosen for better visualization and is not part of our method output.

Text-to-Motion Generation

A person quickly extends one hand at a time, usually alternating between right and left.

A man is walking up the stairs while holding the railing with their left hand.

A person raises his right arm up in front of his face, and holds it there for a second before putting his arm back down.

We show more visual results of our motion generation based on text descriptions. Our method performs accurate kinematic body-part control, like left hand, right arm, up, down etc.

Text-Motion Editing

We can re-enact the same character by editing the text descriptions to generate different gestures.

Motion Trajectory Control

We can control any joint motions by providing trajectory during motion generation.

Ablation Study

A man bends his knees in a squatting motion while holding a bar over his shoulders with both hands.

We generate motion based on (a) only global semantic, (b) global with joint semantic, and (c) global, joint, and interaction semantics, compared with (d) ground truth, to show the importance of our hierarchical text-motion alignment.

BibTeX

@inproceedings{kinmo2025kinematicawarehumanmotion,
      title={{KinMo: Kinematic-aware Human Motion Understanding and Generation}},
      author={Pengfei Zhang and Pinxin Liu and Pablo Garrido and Hyeongwoo Kim and Bindita Chaudhuri},
      booktitle={IEEE/CVF International Conference on Computer Vision},
      year={2025},
}