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

TL;DR

KptLLM++ is a large multimodal language model designed for generic keypoint comprehension, integrating diverse modalities and employing a novel identify-then-detect paradigm to achieve state-of-the-art performance in fine-grained image analysis.

Contribution

The paper introduces KptLLM++, a new multimodal model that unifies keypoint detection across various contexts using a structured reasoning approach and extensive training data.

Findings

Achieves state-of-the-art results on multiple keypoint detection benchmarks.

Demonstrates effective generalization across diverse objects and scenarios.

Enhances human-AI collaboration through a flexible keypoint understanding interface.

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.