Pose estimation is a class of methods that use computer vision techniques to analyze the spatial structure of target objects in images or videos. Its goal is to locate keypoints of a human, animal, or object and infer joint connections or overall pose. This technology involves areas such as object detection, visual representation learning, and geometric modeling, and is a fundamental task in human behavior analysis, intelligent interaction, and 3D visual understanding.

The development of pose estimation is built upon long-term research in multiple fields, including computer vision, human modeling, and image analysis. Early methods primarily relied on manually designed features, template matching, and geometric models to detect key human points, but these methods faced significant limitations under complex backgrounds, occlusion, and pose variations. With the development of deep learning, researchers began to utilize neural networks to directly learn human structural features from image data. In 2014, Google researchers Alexander Toshev and Christian Szegedy published a paper... DeepPose: Human Pose Estimation via Deep Neural Networks The paper proposes a method for human pose estimation using deep neural networks, which transforms pose estimation into a regression problem from images to key point coordinates. This has promoted the application of deep learning in human pose analysis tasks and has become one of the important representative studies in the field of deep pose estimation.

The core task of pose estimation is to locate the positions of various joints in the human body (such as key points like shoulders, elbows, wrists, knees, and ankles) from images or videos, and further reconstruct the complete skeletal structure and body posture. Depending on the coordinate dimension and scene complexity, pose estimation can be divided into 2D pose estimation, 3D pose estimation, and multi-person pose estimation, etc. Technically, it integrates keypoint detection networks, Transformer structures, and 3D vision methods. In recent years, it has gradually moved from multi-stage processes to end-to-end solutions, extending to more complete human morphology reconstruction capabilities such as full-body keypoint estimation and 3D human mesh reconstruction. Currently, this technology is widely used in motion analysis, human-computer interaction, VR/AR, intelligent monitoring, and robot perception scenarios.