  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 Camera
Supervised (ISCS) setting, e.g., a surveillance system deployed across distant
scenes. To handle this challenging problem, a new pipeline is introduced by
synthesizing the cross-camera samples in the feature space for model training.
Specifically, the feature encoder and generator are end-to-end optimized under
a novel method, Camera-Conditioned Stable Feature Generation (CCSFG). Its joint
learning procedure raises concern on the stability of generative model
training. Therefore, a new feature generator, $\sigma$-Regularized Conditional
Variational Autoencoder ($\sigma$-Reg.~CVAE), is proposed with theoretical and
experimental analysis on its robustness. Extensive experiments on two ISCS
person Re-ID datasets demonstrate the superiority of our CCSFG to the
competitors.
