Encoder-Free Human Motion Understanding via Structured Motion Descriptions

2026-04-23 · Source: Takara TLDR - Daily AI Papers · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, medium

Summary

A new approach called Structured Motion Description (SMD) converts joint position sequences into structured natural language descriptions of human motion, enabling large language models (LLMs) to understand and reason about movement without dedicated motion encoders. Inspired by biomechanical analysis, SMD deterministically describes joint angles, body part movements, and global trajectories as text. This method allows LLMs to directly apply their pretrained knowledge of body parts, spatial directions, and movement semantics. SMD achieves state-of-the-art results, scoring 66.7% on BABEL-QA and 90.1% on HuMMan-QA for motion question answering, and R@1 of 0.584 and CIDEr of 53.16 on HumanML3D for motion captioning, outperforming all prior methods. It also offers practical benefits, including cross-LLM compatibility with lightweight LoRA adaptation across 8 LLMs from 6 families, and interpretable attention analysis due to its human-readable output.

Key takeaway

For research scientists developing human motion understanding systems, SMD offers a novel, encoder-free paradigm that significantly improves performance and interpretability. You should explore integrating SMD's text-based motion descriptions into your LLM workflows to leverage pretrained linguistic knowledge directly, potentially simplifying model architectures and enhancing cross-model compatibility. Consider its application for motion question answering and captioning tasks to achieve superior results.

Key insights

Structured Motion Description (SMD) enables LLMs to understand human motion directly from text, bypassing dedicated motion encoders.

Principles

• Represent motion as text for LLM reasoning.
• Deterministic rules convert kinematics to language.

Method

SMD converts joint position sequences into structured natural language descriptions of joint angles, body part movements, and global trajectories, allowing LLMs to process motion as text.

In practice

• Achieves SOTA on motion QA and captioning.
• Works across 8 LLMs with LoRA adaptation.
• Enables interpretable attention analysis.

Topics

• Structured Motion Description
• Human Motion Understanding
• Large Language Models
• Motion Question Answering
• Motion Captioning

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.