Papers with Code 아카이브

Global 3D Human Pose Estimation

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

하위 태스크
0
평가표
1
보존된 평가 기록
1

Global 3D Human Pose Estimation is extending RGB-based human pose estimation to capture errors in global instead of camera-relative coordinate frames. For monocular settings, this task was first introduced by GLAMR (Yuan et al., CVPR 2022).

데이터셋

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EMDB

EMDB contains in-the-wild videos of human activity recorded with a hand-held iPhone. It features reference SMPL body pose and shape parameters, as well as global body root and camera trajectories. The reference 3D poses were obtained by jointly fitting SMPL to 12 body-worn electromagnetic sensors and image data. For the latter we fit a neural implicit avatar model to allow for a dense pixel-wise fitting objective. EMDB contains: * 81 sequences * 105 000 frames * 10 actors (5 female, 5 male) * Global camera trajectories * SMPL pose and shape parameters * 2D Keypoints The dataset can be used to evaluate the following tasks: * Camera-relative 3D human pose and shape estimation from monocular videos. * Global 3D human pose and shape estimation including camera trajectories from monocular videos. * Human motion prediction.

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.