FDC-pose: Enhancing multi-person pose estimation in complex scenes via dynamic synergy mechanisms

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Abstract

Real-time multi-person pose estimation suffers from inter-instance occlusion and severe scale variations. To resolve these bottlenecks, we present focal-dynamic context (FDC)-pose, an efficient single-stage architecture tailored for crowded scenes. The framework incorporates three sequential innovations: 1) a cascaded pyramid network-focal omni-dimensional dynamic convolution (CPN-FocalOD) backbone combining context modulation with omni-dimensional dynamic filtering to disentangle overlapping silhouettes; 2) a cross-scale fusion network featuring content-aware upsampling (DySample) to suppress quantization errors and preserve distal joint details; and 3) a minimum point distance intersection over union (MPDIoU) regression loss enforcing point-level geometric constraints to eliminate occlusion-induced localization jitter. Evaluations on the common objects in context (COCO) 2017 dataset demonstrate that FDC-pose yields absolute gains of 3.4% in AP, 3.0% in AP₅₀, and 4.2% in the strict AP₇₅ metric relative to the baseline. Parallel evaluations on the CrowdPose dataset show that our framework presents a prominent robustness gain, yielding an absolute improvement of 4.5% in AP.

Keywords

human pose estimation / YOLO / dynamic convolution / multi-scale feature fusion / content-aware upsampling / bounding box regression

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