Fine-grained Motion Retrieval via Joint-Angle Motion Images and Token-Patch Late Interaction

2026-03-10

AI Summary

This work addresses the limitations of existing text-to-action retrieval methods, which rely on global embeddings and struggle to capture fine-grained local correspondences, thereby constraining both retrieval accuracy and interpretability. To overcome this, the authors propose a structured pseudo-image representation derived from joint angle sequences, integrated with a pretrained vision Transformer. They further introduce an enhanced token-patch late interaction mechanism to enable fine-grained, interpretable bidirectional alignment between text and action. By incorporating Masked Language Modeling as a regularization objective, the method significantly outperforms state-of-the-art approaches on the HumanML3D and KIT-ML datasets, achieving higher retrieval accuracy while enabling visual analysis of localized semantic correspondences.

Technology Category

• Computer Vision: Image and Video Retrieval
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.
• Machine Learning: Representation Learning

Application Category

• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Text-motion retrieval aims to learn a semantically aligned latent space between natural language descriptions and 3D human motion skeleton sequences, enabling bidirectional search across the two modalities. Most existing methods use a dual-encoder framework that compresses motion and text into global embeddings, discarding fine-grained local correspondences, and thus reducing accuracy. Additionally, these global-embedding methods offer limited interpretability of the retrieval results. To overcome these limitations, we propose an interpretable, joint-angle-based motion representation that maps joint-level local features into a structured pseudo-image, compatible with pre-trained Vision Transformers. For text-to-motion retrieval, we employ MaxSim, a token-wise late interaction mechanism, and enhance it with Masked Language Modeling regularization to foster robust, interpretable text-motion alignment. Extensive experiments on HumanML3D and KIT-ML show that our method outperforms state-of-the-art text-motion retrieval approaches while offering interpretable fine-grained correspondences between text and motion. The code is available in the supplementary material.

Problem

• text-motion retrieval
• fine-grained correspondence
• global embedding
• interpretability
• 3D human motion

Innovation

• joint-angle motion image
• token-patch late interaction
• fine-grained motion retrieval
• interpretable alignment
• Vision Transformer