  Reconstructing 3D human pose and shape from monocular videos is a
well-studied but challenging problem. Common challenges include occlusions, the
inherent ambiguities in the 2D to 3D mapping and the computational complexity
of video processing. Existing methods ignore the ambiguities of the
reconstruction and provide a single deterministic estimate for the 3D pose. In
order to address these issues, we present a Temporal Attention based
Probabilistic human pose and shape Estimation method (TAPE) that operates on an
RGB video. More specifically, we propose to use a neural network to encode
video frames to temporal features using an attention-based neural network.
Given these features, we output a per-frame but temporally-informed probability
distribution for the human pose using Normalizing Flows. We show that TAPE
outperforms state-of-the-art methods in standard benchmarks and serves as an
effective video-based prior for optimization-based human pose and shape
estimation. Code is available at: https: //github.com/nikosvasilik/TAPE
