KptLLM++: Towards Generic Keypoint Comprehension with Large Language Model

2025-07-15

AI Summary

Multimodal large language models (MLLMs) remain limited in fine-grained image understanding—particularly for deformable object keypoint localization. To address this, we propose the first general-purpose keypoint understanding framework, introducing a novel “identify-then-detect” paradigm and structured chain-of-thought reasoning to achieve unified, cross-scene and cross-category keypoint localization. Our method jointly leverages instruction-driven semantic parsing and pixel-level keypoint regression, trained on a large-scale, multi-category dataset comprising over 500K samples. Evaluated on multiple benchmarks, it achieves state-of-the-art performance, significantly improving localization accuracy and generalization under complex occlusions and diverse object appearances. Moreover, it enhances semantic controllability in human–AI collaborative interaction by enabling precise, instruction-guided keypoint interpretation.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Multi-modal Vision
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

The emergence of Multimodal Large Language Models (MLLMs) has revolutionized image understanding by bridging textual and visual modalities. However, these models often struggle with capturing fine-grained semantic information, such as the precise identification and analysis of object keypoints. Keypoints, as structure-aware, pixel-level, and compact representations of objects, particularly articulated ones, play a crucial role in applications such as fine-grained image analysis, object retrieval, and behavior recognition. In this paper, we propose KptLLM++, a novel multimodal large language model that specifically designed for generic keypoint comprehension through the integration of diverse input modalities guided by user-defined instructions. By unifying keypoint detection across varied contexts, KptLLM++ establishes itself as an advanced interface, fostering more effective human-AI collaboration. The model is built upon a novel identify-then-detect paradigm, which first interprets keypoint semantics and subsequently localizes their precise positions through a structured chain-of-thought reasoning mechanism. To push the boundaries of performance, we have scaled up the training dataset to over 500K samples, encompassing diverse objects, keypoint categories, image styles, and scenarios with complex occlusions. This extensive scaling enables KptLLM++ to unlock its potential, achieving remarkable accuracy and generalization. Comprehensive experiments on multiple keypoint detection benchmarks demonstrate its state-of-the-art performance, underscoring its potential as a unified solution for fine-grained image understanding and its transformative implications for human-AI interaction.

Problem

• Enhancing fine-grained keypoint detection in images
• Unifying keypoint comprehension across diverse object contexts
• Improving human-AI collaboration via structured keypoint reasoning

Innovation

• Integrates diverse input modalities for keypoint comprehension
• Uses identify-then-detect paradigm with chain-of-thought reasoning
• Scales training to 500K samples for better accuracy