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Niaz Ahmad

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2 papers

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IJCAI Conference 2025 Conference Paper

Keypoints as Dynamic Centroids for Unified Human Pose and Segmentation

Niaz Ahmad
Jawad Khan
Kang G. Shin
Youngmoon Lee
Guanghui Wang

The dynamic movement of the human body presents a fundamental challenge for human pose estimation and body segmentation. State-of-the-art approaches primarily rely on combining keypoint heatmaps with segmentation masks, but often struggle in scenarios involving overlapping joints during pose estimation or rapidly changing poses for instance-level segmentation. To address these limitations, we leverage Keypoints as Dynamic Centroid (KDC), a new centroid-based representation for unified human pose estimation and instance-level segmentation. KDC adopts a bottom-up paradigm to generate keypoint heatmaps for easily distinguishable and complex keypoints, and improves keypoint detection and confidence scores by introducing KeyCentroids using a keypoint disk. It leverages high-confidence keypoints as dynamic centroids in the embedding space to generate MaskCentroids, allowing for the swift clustering of pixels to specific human instances during rapid changes in human body movements in a live environment. Our experimental evaluations focus on crowded and occluded cases using the CrowdPose, OCHuman, and COCO benchmarks, demonstrating KDC’s effectiveness and generalizability in challenging scenarios in terms of both accuracy and runtime performance. Our implementation is available at https: //sites. google. com/view/niazahmad/projects/kdc.

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AAAI Conference 2022 Conference Paper

Joint Human Pose Estimation and Instance Segmentation with PosePlusSeg

Niaz Ahmad
Jawad Khan
Jeremy Yuhyun Kim
Youngmoon Lee

Despite the advances in multi-person pose estimation, stateof-the-art techniques only deliver the human pose structure. Yet, they do not leverage the keypoints of human pose to deliver whole-body shape information for human instance segmentation. This paper presents PosePlusSeg, a joint model designed for both human pose estimation and instance segmentation. For pose estimation, PosePlusSeg first takes a bottom-up approach to detect the soft and hard keypoints of individuals by producing a strong keypoint heat map, then improves the keypoint detection confidence score by producing a body heat map. For instance segmentation, PosePlusSeg generates a mask offset where keypoint is defined as a centroid for the pixels in the embedding space, enabling instancelevel segmentation for the human class. Finally, we propose a new pose and instance segmentation algorithm that enables PosePlusSeg to determine the joint structure of the human pose and instance segmentation. Experiments using the COCO challenging dataset demonstrate that PosePlusSeg copes better with challenging scenarios, like occlusions, entangled limbs, and overlapped people. PosePlusSeg outperforms state-of-the-art detection-based approaches achieving a 0. 728 mAP for human pose estimation and a 0. 445 mAP for instance segmentation. Code has been made available at https: //github. com/RaiseLab/PosePlusSeg.

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