MotionLLM: Understanding Human Behaviors from Human Motions and Videos

Ling-Hao Chen😎 1, 3, Shunlin Lu😎 2, 3,
Ailing Zeng3, Hao Zhang3, 4, Benyou Wang2, Ruimao Zhang2, Lei Zhang🤗 3

😎Co-first author. Listing order is random.
🤗Corresponding author.
1Tsinghua University
2School of Data Science, Shenzhen Research Institute of Big Data, CUHK (SZ)
3International Digital Economy Academy (IDEA)
4The Hong Kong University of Science and Technology

📖 Abstract

This study delves into the realm of multi-modality (i.e., video and motion modalities) human behavior understanding by leveraging the powerful capabilities of Large Language Models (LLMs). Diverging from recent LLMs designed for video-only or motion-only understanding, we argue that understanding human behavior necessitates joint modeling from both videos and motion sequences (e.g., SMPL sequences) to capture nuanced body part dynamics and semantics effectively. In light of this, we present MotionLLM, a straightforward yet effective framework for human motion understanding, captioning, and reasoning. Specifically, MotionLLM adopts a unified video-motion training strategy that leverages the complementary advantages of existing coarse video-text data and fine-grained motion-text data to glean rich spatial-temporal insights. Furthermore, we collect a substantial dataset, MoVid, comprising diverse videos, motions, captions, and instructions. Additionally, we propose the MoVid-Bench, with carefully manual annotations, for better evaluation of human behavior understanding on video and motion. Extensive experiments show the superiority of MotionLLM in the caption, spatial-temporal comprehension, and reasoning ability.

🤩 Introducing MotionLLM

🤩 Interesting Results

✌️ More Features

🤝 Motion and Video Data Help with each other!

🤗 Live Demo

👀 System Overview

🦾 Technical Detail Comparison

🫡 Comparison with Video Understanding Baselines

💪 Comparison with Motion Captioning Baselines

🌹 Acknowledgement


  title={MotionLLM: Understanding Human Behaviors from Human Motions and Videos},
  author={Chen, Ling-Hao and Lu, Shunlin and Zeng, Ailing and Zhang, Hao and Wang, Benyou and Zhang, Ruimao and Zhang, Lei},

The website template was adapted from HumanMAC Project.