3D Human Reconstruction

32 papers with code • 5 benchmarks • 7 datasets

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Most implemented papers

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

facebookresearch/pifuhd CVPR 2020

Although current approaches have demonstrated the potential in real world settings, they still fail to produce reconstructions with the level of detail often present in the input images.

3D Human Mesh Regression with Dense Correspondence

zengwang430521/DecoMR CVPR 2020

This paper proposes a model-free 3D human mesh estimation framework, named DecoMR, which explicitly establishes the dense correspondence between the mesh and the local image features in the UV space (i. e. a 2D space used for texture mapping of 3D mesh).

PyMAF: 3D Human Pose and Shape Regression with Pyramidal Mesh Alignment Feedback Loop

HongwenZhang/PyMAF ICCV 2021

Regression-based methods have recently shown promising results in reconstructing human meshes from monocular images.

ICON: Implicit Clothed humans Obtained from Normals

yuliangxiu/icon CVPR 2022

First, ICON infers detailed clothed-human normals (front/back) conditioned on the SMPL(-X) normals.

SmoothNet: A Plug-and-Play Network for Refining Human Poses in Videos

cure-lab/SmoothNet 27 Dec 2021

With a simple yet effective motion-aware fully-connected network, SmoothNet improves the temporal smoothness of existing pose estimators significantly and enhances the estimation accuracy of those challenging frames as a side-effect.

Self-supervised Learning of Motion Capture

chingswy/HumanPoseMemo NeurIPS 2017

In this work, we propose a learning based motion capture model for single camera input.

DeepHuman: 3D Human Reconstruction from a Single Image

ZhengZerong/DeepHuman ICCV 2019

We propose DeepHuman, an image-guided volume-to-volume translation CNN for 3D human reconstruction from a single RGB image.

Expressive Body Capture: 3D Hands, Face, and Body from a Single Image

vchoutas/smplify-x CVPR 2019

We use the new method, SMPLify-X, to fit SMPL-X to both controlled images and images in the wild.

PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization

shunsukesaito/PIFu ICCV 2019

We introduce Pixel-aligned Implicit Function (PIFu), a highly effective implicit representation that locally aligns pixels of 2D images with the global context of their corresponding 3D object.

Learning to Dress 3D People in Generative Clothing

QianliM/CAPE CVPR 2020

To our knowledge, this is the first generative model that directly dresses 3D human body meshes and generalizes to different poses.