CapeX: Category-Agnostic Pose Estimation from Textual Point Explanation

TL;DR

This paper introduces a text-based, category-agnostic pose estimation method that uses textual keypoint descriptions and a pose-graph, outperforming previous image-based approaches and advancing the state-of-the-art on a diverse benchmark.

Contribution

It proposes a novel text-based approach for category-agnostic pose estimation, eliminating the need for support images and improving generalization and occlusion handling.

Findings

Achieves a 1.07% performance boost on MP-100 benchmark.

Introduces text description annotations to enrich dataset utility.

Establishes a new state-of-the-art in 1-shot CAPE.

Abstract

Conventional 2D pose estimation models are constrained by their design to specific object categories. This limits their applicability to predefined objects. To overcome these limitations, category-agnostic pose estimation (CAPE) emerged as a solution. CAPE aims to facilitate keypoint localization for diverse object categories using a unified model, which can generalize from minimal annotated support images. Recent CAPE works have produced object poses based on arbitrary keypoint definitions annotated on a user-provided support image. Our work departs from conventional CAPE methods, which require a support image, by adopting a text-based approach instead of the support image. Specifically, we use a pose-graph, where nodes represent keypoints that are described with text. This representation takes advantage of the abstraction of text descriptions and the structure imposed by the graph. Our approach effectively breaks symmetry, preserves structure, and improves occlusion handling. We validate our novel approach using the MP-100 benchmark, a comprehensive dataset spanning over 100 categories and 18,000 images. Under a 1-shot setting, our solution achieves a notable performance boost of 1.07\%, establishing a new state-of-the-art for CAPE. Additionally, we enrich the dataset by providing text description annotations, further enhancing its utility for future research.