https://github.com/MandyMo/pytorch_HMR
Unite the People: Closing the Loop Between 3D and 2D Human Representations
Papers with Code 아카이브
3D human pose and shape estimation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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- 보존된 평가 기록
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Estimate 3D human pose and shape (e.g. SMPL) from images
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/classner/up
Unite the People: Closing the Loop Between 3D and 2D Human Representations
https://github.com/jeff-sjtu/dnd
https://github.com/jeffffffli/HybrIK
https://github.com/nkolot/GraphCMR
Convolutional Mesh Regression for Single-Image Human Shape Reconstruction
https://github.com/pixelite1201/CameraHMR
https://github.com/redrock303/HEMlets
HEMlets PoSh: Learning Part-Centric Heatmap Triplets for 3D Human Pose and Shape Estimation
https://github.com/xiuyu0000/new_papers_codes/tree/main/CLIFF
CLIFF: Carrying Location Information in Full Frames into Human Pose and Shape Estimation
https://github.com/xiuyu0000/new_papers_codes/tree/main/cmr
Convolutional Mesh Regression for Single-Image Human Shape Reconstruction
https://github.com/xuxy09/texformer
3D Human Pose, Shape and Texture from Low-Resolution Images and Videos
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.