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To learn camera-view invariant features for person Re-IDentification (Re-ID),the cross-camera image pairs of each person play an important role. However,such cross-view training samples could be unavailable under the ISolated CameraSupervised (ISCS) setting, e.g., a surveillance system deployed across distantscenes. To handle this challenging problem, a new pipeline is introduced bysynthesizing the cross-camera samples in the feature space for model training.Specifically, the feature encoder and generator are end-to-end optimized undera novel method, Camera-Conditioned Stable Feature Generation (CCSFG). Its jointlearning procedure raises concern on the stability of generative modeltraining. Therefore, a new feature generator, $\sigma$-Regularized ConditionalVariational Autoencoder ($\sigma$-Reg.~CVAE), is proposed with theoretical andexperimental analysis on its robustness. Extensive experiments on two ISCSperson Re-ID datasets demonstrate the superiority of our CCSFG to thecompetitors.

Camera-Conditioned Stable Feature Generation for Isolated Camera Supervised Person Re-IDentification