  In this paper, we introduce a global video representation to video-based
person re-identification (re-ID) that aggregates local 3D features across the
entire video extent. Most of the existing methods rely on 2D convolutional
networks (ConvNets) to extract frame-wise deep features which are pooled
temporally to generate the video-level representations. However, 2D ConvNets
lose temporal input information immediately after the convolution, and a
separate temporal pooling is limited in capturing human motion in shorter
sequences. To this end, we present a \textit{global} video representation (3D
PersonVLAD), complementary to 3D ConvNets as a novel layer to capture the
appearance and motion dynamics in full-length videos. However, encoding each
video frame in its entirety and computing an aggregate global representation
across all frames is tremendously challenging due to occlusions and
misalignments. To resolve this, our proposed network is further augmented with
3D part alignment module to learn local features through soft-attention module.
These attended features are statistically aggregated to yield
identity-discriminative representations. Our global 3D features are
demonstrated to achieve state-of-the-art results on three benchmark datasets:
MARS \cite{MARS}, iLIDS-VID \cite{VideoRanking}, and PRID 2011
