# ViMoNet: A Multimodal Vision-Language Framework for Human Behavior Understanding from Motion and Video

arXiv: https://arxiv.org/abs/2508.09818 · HTML: https://mlsanity.com/paper/2508.09818/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 32 references that are in the map.
- First posted on arXiv 13 Aug 2025.

## Builds on (most cited first, 20 of 20)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
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- [Executing your Commands via Motion Diffusion in Latent Space](https://mlsanity.com/paper/2212.04048/) (2022)
- [TM2T: Stochastic and Tokenized Modeling for the Reciprocal Generation of 3D Human Motions and Texts](https://mlsanity.com/paper/2207.01696/) (2022)
- [Language2Pose: Natural Language Grounded Pose Forecasting](https://mlsanity.com/paper/1907.01108/) (2019)
- [Synthesis of Compositional Animations from Textual Descriptions](https://mlsanity.com/paper/2103.14675/) (2021)
- [VITA: Towards Open-Source Interactive Omni Multimodal LLM](https://mlsanity.com/paper/2408.05211/) (2024)
- [VideoLLM: Modeling Video Sequence with Large Language Models](https://mlsanity.com/paper/2305.13292/) (2023)
- [Mini-Omni2: Towards Open-source GPT-4o with Vision, Speech and Duplex Capabilities](https://mlsanity.com/paper/2410.11190/) (2024)
- [Text2Action: Generative Adversarial Synthesis from Language to Action](https://mlsanity.com/paper/1710.05298/) (2017)
- [HumanMAC: Masked Motion Completion for Human Motion Prediction](https://mlsanity.com/paper/2302.03665/) (2023)
- [Video ChatCaptioner: Towards Enriched Spatiotemporal Descriptions](https://mlsanity.com/paper/2304.04227/) (2023)
- [Motion Question Answering via Modular Motion Programs](https://mlsanity.com/paper/2305.08953/) (2023)
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- [Privacy Preserving Machine Learning Model Personalization through Federated Personalized Learning](https://mlsanity.com/paper/2505.01788/) (2025)
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- [Advancing Exchange Rate Forecasting: Leveraging Machine Learning and AI for Enhanced Accuracy in Global Financial Markets](https://mlsanity.com/paper/2506.09851/) (2025)

## Abstract (arXiv)

This study investigates the use of large language models (LLMs) for human behavior understanding by jointly leveraging motion and video data. We argue that integrating these complementary modalities is essential for capturing both fine-grained motion dynamics and contextual semantics of human actions, addressing the limitations of prior motion-only or video-only approaches. To this end, we propose ViMoNet, a multimodal vision-language framework trained through a two-stage alignment and instruction-tuning strategy that combines precise motion-text supervision with large-scale video-text data. We further introduce VIMOS, a multimodal dataset comprising human motion sequences, videos, and instruction-level annotations, along with ViMoNet-Bench, a standardized benchmark for evaluating behavior-centric reasoning. Experimental results demonstrate that ViMoNet consistently outperforms existing methods across caption generation, motion understanding, and human behavior interpretation tasks. The proposed framework shows significant potential in assistive healthcare applications, such as elderly monitoring, fall detection, and early identification of health risks in aging populations. This work contributes to the United Nations Sustainable Development Goal 3 (SDG 3: Good Health and Well-being) by enabling accessible AI-driven tools that promote universal health coverage, reduce preventable health issues, and enhance overall well-being.