Exploiting temporal information for 3D human pose estimation

ECCV 2018 Mir Rayat Imtiaz HossainJames J. Little

In this work, we address the problem of 3D human pose estimation from a sequence of 2D human poses. Although the recent success of deep networks has led many state-of-the-art methods for 3D pose estimation to train deep networks end-to-end to predict from images directly, the top-performing approaches have shown the effectiveness of dividing the task of 3D pose estimation into two steps: using a state-of-the-art 2D pose estimator to estimate the 2D pose from images and then mapping them into 3D space... (read more)

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Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK LEADERBOARD
3D Human Pose Estimation HumanEva-I Sequence-to-sequence network Mean Reconstruction Error (mm) 22.0 # 3