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We propose a sparse end-to-end multi-person pose regression framework, termedQueryPose, which can directly predict multi-person keypoint sequences from theinput image. The existing end-to-end methods rely on dense representations topreserve the spatial detail and structure for precise keypoint localization.However, the dense paradigm introduces complex and redundant post-processesduring inference. In our framework, each human instance is encoded by severallearnable spatial-aware part-level queries associated with an instance-levelquery. First, we propose the Spatial Part Embedding Generation Module (SPEGM)that considers the local spatial attention mechanism to generate severalspatial-sensitive part embeddings, which contain spatial details and structuralinformation for enhancing the part-level queries. Second, we introduce theSelective Iteration Module (SIM) to adaptively update the sparse part-levelqueries via the generated spatial-sensitive part embeddings stage-by-stage.Based on the two proposed modules, the part-level queries are able to fullyencode the spatial details and structural information for precise keypointregression. With the bipartite matching, QueryPose avoids the hand-designedpost-processes and surpasses the existing dense end-to-end methods with 73.6 APon MS COCO mini-val set and 72.7 AP on CrowdPose test set. Code is available athttps://github.com/buptxyb666/QueryPose.

QueryPose: Sparse Multi-Person Pose Regression via Spatial-Aware Part-Level Query