We present the global deep video representation learning to video-based person reidentification (re-ID) that aggregates local 3-D features across the entire video extent. Existing methods typically extract frame-wise deep features from 2-D convolutional networks (ConvNets) which are pooled temporally to produce the video-level representations. However, 2-D ConvNets lose temporal priors immediately after the convolutions, and a separate temporal pooling is limited in capturing human motion in short sequences. In this paper, we present global video representation learning, to be complementary to 3-D ConvNets as a novel layer to capture the appearance and motion dynamics in full-length videos. Nevertheless, encoding each video frame in its entirety and computing aggregate global representations across all frames is tremendously challenging due to the occlusions and misalignments. To resolve this, our proposed network is further augmented with the 3-D part alignment to learn local features through the soft-attention module. These attended features are statistically aggregated to yield identity-discriminative representations. Our global 3-D features are demonstrated to achieve the state-of-the-art results on three benchmark data sets: MARS, Imagery Library for Intelligent Detection Systems-Video Re-identification, and PRID2011.