  Despite significant progress in single image-based 3D human mesh recovery,
accurately and smoothly recovering 3D human motion from a video remains
challenging. Existing video-based methods generally recover human mesh by
estimating the complex pose and shape parameters from coupled image features,
whose high complexity and low representation ability often result in
inconsistent pose motion and limited shape patterns. To alleviate this issue,
we introduce 3D pose as the intermediary and propose a Pose and Mesh
Co-Evolution network (PMCE) that decouples this task into two parts: 1)
video-based 3D human pose estimation and 2) mesh vertices regression from the
estimated 3D pose and temporal image feature. Specifically, we propose a
two-stream encoder that estimates mid-frame 3D pose and extracts a temporal
image feature from the input image sequence. In addition, we design a
co-evolution decoder that performs pose and mesh interactions with the
image-guided Adaptive Layer Normalization (AdaLN) to make pose and mesh fit the
human body shape. Extensive experiments demonstrate that the proposed PMCE
outperforms previous state-of-the-art methods in terms of both per-frame
accuracy and temporal consistency on three benchmark datasets: 3DPW, Human3.6M,
and MPI-INF-3DHP. Our code is available at https://github.com/kasvii/PMCE.
