Consensus-Anchored Retrieval with Cross-Modal Alignment and Semantic Calibration for Sketch-Based Person Re-Identification

Sketch-based person re-identification is challenged by both the sketch–photo modality gap and strong sketch subjectivity, as sketches of the same identity may exhibit substantial style-dependent variations. Existing methods mainly fuse multiple sketches or suppress style discrepancies, but do not explicitly model the latent identity consensus shared across style-biased observations. We therefore propose a consensus-anchored retrieval framework for robust sketch-based person re-identification. Its core component, Style-Conditioned Prototype-Fusion Consensus (SPFC), jointly learns an identity-consensus prototype and a query-adaptive retrieval descriptor through two complementary style-conditioned estimators. Two auxiliary training objectives further support this representation: Global-to-Local Mutual Alignment (GLMA) uses the global descriptor of each modality to guide local aggregation in the other, while Semantic Affinity-Guided Contrastive Calibration (SACC) uses auxiliary identity-level semantics available only during training to calibrate ambiguous negatives. Inference remains entirely visual. Extensive experiments under single-query, multi-query, and cross-style protocols demonstrate the effectiveness and generalizability of the proposed framework. Upon acceptance, we will publicly release the source code and pretrained checkpoints on GitHub.