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.

传统的 2D 姿势估计模型受其设计的限制，仅适用于特定的物体类别，为了克服这些限制，通用类别姿势估计（CAPE）作为解决方案出现，CAPE 旨在使用统一模型为多样的物体类别进行关键点定位，可以从最小的注释支持图像中进行泛化，我们的方法采用了一种不同于常规 CAPE 方法的文本化方法，而不是使用支持图像，具体而言，我们使用姿势图，其中节点表示使用文本进行描述的关键点，这种表示利用文本描述的抽象和图所施加的结构，我们的方法有效地打破了对称性，保留了结构并改善了遮挡处理，我们使用 MP-100 基准验证了我们的新方法，该数据集涵盖 100 多个类别和 18,000 张图像，在 1-shot 设置下，我们的解决方案的性能提升了 1.07％，确立了 CAPE 的新的最先进技术水平，此外，我们通过提供文本描述注释来丰富数据集，进一步增强其对未来研究的实用性。