Papers with Code 아카이브

3D Human Shape Estimation

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평가표

2

보존된 평가 기록

12

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/Healthcare-Robotics/bodies-at-rest

공식 코드

Bodies at Rest: 3D Human Pose and Shape Estimation from a Pressure Image using Synthetic Data

https://github.com/MoyGcc/hpcwild

공식 코드

Human Performance Capture from Monocular Video in the Wild

https://github.com/Project-Splinter/MonoPort

공식 코드

Monocular Real-Time Volumetric Performance Capture

https://github.com/Unity-Technologies/AnthroNet

공식 코드

AnthroNet: Conditional Generation of Humans via Anthropometrics

https://github.com/Unity-Technologies/com.unity.cv.synthetichumans

공식 코드

AnthroNet: Conditional Generation of Humans via Anthropometrics

https://github.com/akashsengupta1997/STRAPS-3DHumanShapePose

공식 코드

Synthetic Training for Accurate 3D Human Pose and Shape Estimation in the Wild

https://github.com/akashsengupta1997/hierarchicalprobabilistic3dhuman

공식 코드

Hierarchical Kinematic Probability Distributions for 3D Human Shape and Pose Estimation from Images in the Wild

https://github.com/boqian-li/ETCH

공식 코드

ETCH: Generalizing Body Fitting to Clothed Humans via Equivariant Tightness

https://github.com/facebookresearch/pifuhd

공식 코드

PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization

https://github.com/iGame-Lab/LASOR

공식 코드

LASOR: Learning Accurate 3D Human Pose and Shape Via Synthetic Occlusion-Aware Data and Neural Mesh Rendering

https://github.com/jfzhang95/BMP

공식 코드

Body Meshes as Points

https://github.com/marcopesavento/Super-resolution-3D-Human-Shape-from-a-Single-Low-Resolution-Image

공식 코드

Super-resolution 3D Human Shape from a Single Low-Resolution Image

https://github.com/muelea/shapy

공식 코드

Accurate 3D Body Shape Regression using Metric and Semantic Attributes

https://github.com/nicolasugrinovic/size_depth_disambiguation

공식 코드

Body Size and Depth Disambiguation in Multi-Person Reconstruction from Single Images

https://github.com/pixelite1201/agora_evaluation

공식 코드

AGORA: Avatars in Geography Optimized for Regression Analysis

https://github.com/yuliangxiu/icon

공식 코드

ICON: Implicit Clothed humans Obtained from Normals

https://github.com/zczcwh/gtrs

공식 코드

A Lightweight Graph Transformer Network for Human Mesh Reconstruction from 2D Human Pose

https://github.com/2023-MindSpore-1/ms-code-27

End-to-end Recovery of Human Shape and Pose

https://github.com/2023-MindSpore-4/Code-5/tree/main/HMR

End-to-end Recovery of Human Shape and Pose

https://github.com/Liuxiang0358/HMR

End-to-end Recovery of Human Shape and Pose

https://github.com/MandyMo/pytorch_HMR

End-to-end Recovery of Human Shape and Pose

https://github.com/ManifoldFR/recvis-project

End-to-end Recovery of Human Shape and Pose

https://github.com/MindSpore-paper-code-3/code4/tree/main/HMR

End-to-end Recovery of Human Shape and Pose

https://github.com/agnJason/RGB-D-PIFuHD

PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization

https://github.com/akanazawa/hmr

End-to-end Recovery of Human Shape and Pose

https://github.com/anilarmagan/HANDS19-Challenge-Toolbox

End-to-end Recovery of Human Shape and Pose

https://github.com/cardboard-q/pifuhd_demo_model_test

PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization

https://github.com/naver/posebert

Leveraging MoCap Data for Human Mesh Recovery

https://github.com/nkolot/SPIN

Learning to Reconstruct 3D Human Pose and Shape via Model-fitting in the Loop

https://github.com/open-mmlab/mmpose

End-to-end Recovery of Human Shape and Pose

https://github.com/otaheri/GOAL

ICON: Implicit Clothed humans Obtained from Normals

https://github.com/russoale/hmr2.0

End-to-end Recovery of Human Shape and Pose

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