未来への回帰：関節認識型時系列深層学習による3D人体姿勢推定
Back to the Future: Joint Aware Temporal Deep Learning 3D Human Pose Estimation
動画からの3D人体姿勢推定において、より深いCNNフィルタと関節位置・運動の誤差を損失として課すことで、Human3.6Mベンチマークで位置誤差・速度誤差・加速度誤差を従来手法より低減した手法を提案した。
著者: Vikas Gupta
分類: cs.CV, cs.LG, cs.RO
原文アブストラクト
We propose a new deep learning network that introduces a deeper CNN channel filter and constraints as losses to reduce joint position and motion errors for 3D video human body pose estimation. Our model outperforms the previous best result from the literature based on mean per-joint position error, velocity error, and acceleration errors on the Human 3.6M benchmark corresponding to a new state-of-the-art mean error reduction in all protocols and motion metrics. Mean per joint error is reduced by 1%, velocity error by 7% and acceleration by 13% compared to the best results from the literature. Our contribution increasing positional accuracy and motion smoothness in video can be integrated with future end to end networks without increasing network complexity. Our model and code are available at https://vnmr.github.io/ Keywords: 3D, human, image, pose, action, detection, object, video, visual, supervised, joint, kinematic