KptLLM: Unveiling the Power of Large Language Model for Keypoint Comprehension

TL;DR

This work introduces the novel challenge of Semantic Keypoint Comprehension, which aims to comprehend keypoints across different task scenarios, including keypoint semantic understanding, visual prompt-based keypoint detection, and textual prompt-based keypoint detection.

Abstract

Recent advancements in Multimodal Large Language Models (MLLMs) have greatly improved their abilities in image understanding. However, these models often struggle with grasping pixel-level semantic details, e.g., the keypoints of an object. To bridge this gap, we introduce the novel challenge of Semantic Keypoint Comprehension, which aims to comprehend keypoints across different task scenarios, including keypoint semantic understanding, visual prompt-based keypoint detection, and textual prompt-based keypoint detection. Moreover, we introduce KptLLM, a unified multimodal model that utilizes an identify-then-detect strategy to effectively address these challenges. KptLLM underscores the initial discernment of semantics in keypoints, followed by the precise determination of their positions through a chain-of-thought process. With several carefully designed modules, KptLLM adeptly handles various modality inputs, facilitating the interpretation of both semantic contents and keypoint locations. Our extensive experiments demonstrate KptLLM's superiority in various keypoint detection benchmarks and its unique semantic capabilities in interpreting keypoints.

Figures (3)

• Figure 1: This work aims to address the problem of semantic keypoint comprehension, which aims to understand keypoints across different task scenarios: (a) Keypoint Semantic Understanding takes the object image and a keypoint prompt (i.e., the position of the target keypoint) as inputs, then generate responses that interpret keypoint semantics; (b) Visual Prompt-based Keypoint Detection takes a query image and a support image with a keypoint prompt as inputs and then outputs the corresponding keypoint positions and semantics of the query image; (c) Textual Prompt-based Keypoint Detection utilizes detailed descriptions of keypoints through extensive text, to perform more generalizable keypoint detection.
• Figure 2: We introduce KptLLM, a unified framework designed to address three tasks of semantic keypoint comprehension: ① Keypoint Semantic Undertanding, which processes a support image $\mathbf{I}_s$ and a support keypoint prompt $\mathbf{x}$ to generate responses that interpret the semantic information of the specified keypoint; ② Visual Prompt-based Keypoint Detection aims to detect the corresponding keypoint in the query image $\mathbf{I}_q$ based on the understanding of the support keypoint prompt; ③ Textual Prompt-based Keypoint Detection leverages textual keypoint descriptions to directly infer the corresponding keypoint positions in the query image.
• Figure 3: Using the same support image with support keypoints, our model could effectively detect different query images with various poses, object appearances, and environments.