Keypoint-Integrated Instruction-Following Data Generation for Enhanced Human Pose and Action Understanding in Multimodal Models

• URL: http://arxiv.org/abs/2409.09306v2
• Date: Mon, 02 Jun 2025 09:12:50 GMT
• Title: Keypoint-Integrated Instruction-Following Data Generation for Enhanced Human Pose and Action Understanding in Multimodal Models
• Authors: Dewen Zhang, Wangpeng An, Hayaru Shouno,
• Abstract summary: Current vision-language multimodal models are well-adapted for general visual understanding tasks.<n>We introduce a method for generating such data by integrating human keypoints with traditional visual features such as captions and bounding boxes.<n>We fine-tune the LLaVA-1.5-7B model using this dataset and evaluate it on the benchmark, achieving significant improvements.
• Score: 1.9890559505377343
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Current vision-language multimodal models are well-adapted for general visual understanding tasks. However, they perform inadequately when handling complex visual tasks related to human poses and actions due to the lack of specialized vision-language instruction-following data. We introduce a method for generating such data by integrating human keypoints with traditional visual features such as captions and bounding boxes, enabling more precise understanding of human-centric scenes. Our approach constructs a dataset comprising 200,328 samples tailored to fine-tune models for human-centric tasks, focusing on three areas: conversation, detailed description, and complex reasoning. We establish a benchmark called Human Pose and Action Understanding Benchmark (HPAUB) to assess model performance on human pose and action understanding. We fine-tune the LLaVA-1.5-7B model using this dataset and evaluate it on the benchmark, achieving significant improvements. Experimental results show an overall improvement of 21.18% compared to the original LLaVA-1.5-7B model. These findings highlight the effectiveness of keypoint-integrated data in enhancing multimodal models. Code is available at https://github.com/Ody-trek/Keypoint-Instruction-Tuning.

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