ダイナミクス制御による自己中心視点ポーズ推定
Dynamics-Regulated Kinematic Policy for Egocentric Pose Estimation
ウェアラブルカメラ映像から、運動学モデルと物理シミュレーション上のダイナミクスモデルを組み合わせ、物体との物理的に整合する3D人間ポーズを推定する手法を提案した。
著者: Zhengyi Luo, Ryo Hachiuma, Ye Yuan, Kris Kitani
分類: cs.CV, cs.AI, cs.LG, cs.RO
原文アブストラクト
We propose a method for object-aware 3D egocentric pose estimation that tightly integrates kinematics modeling, dynamics modeling, and scene object information. Unlike prior kinematics or dynamics-based approaches where the two components are used disjointly, we synergize the two approaches via dynamics-regulated training. At each timestep, a kinematic model is used to provide a target pose using video evidence and simulation state. Then, a prelearned dynamics model attempts to mimic the kinematic pose in a physics simulator. By comparing the pose instructed by the kinematic model against the pose generated by the dynamics model, we can use their misalignment to further improve the kinematic model. By factoring in the 6DoF pose of objects (e.g., chairs, boxes) in the scene, we demonstrate for the first time, the ability to estimate physically-plausible 3D human-object interactions using a single wearable camera. We evaluate our egocentric pose estimation method in both controlled laboratory settings and real-world scenarios.
