3D Human Shape Estimation

15 papers with code • 2 benchmarks • 5 datasets

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Most implemented papers

End-to-end Recovery of Human Shape and Pose

open-mmlab/mmpose CVPR 2018

The main objective is to minimize the reprojection loss of keypoints, which allow our model to be trained using images in-the-wild that only have ground truth 2D annotations.

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.

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.

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

nkolot/SPIN ICCV 2019

Our approach is self-improving by nature, since better network estimates can lead the optimization to better solutions, while more accurate optimization fits provide better supervision for the network.

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

Healthcare-Robotics/bodies-at-rest CVPR 2020

We describe a physics-based method that simulates human bodies at rest in a bed with a pressure sensing mat, and present PressurePose, a synthetic dataset with 206K pressure images with 3D human poses and shapes.

Monocular Real-Time Volumetric Performance Capture

Project-Splinter/MonoPort ECCV 2020

We present the first approach to volumetric performance capture and novel-view rendering at real-time speed from monocular video, eliminating the need for expensive multi-view systems or cumbersome pre-acquisition of a personalized template model.

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

akashsengupta1997/STRAPS-3DHumanShapePose 21 Sep 2020

Thus, we propose STRAPS (Synthetic Training for Real Accurate Pose and Shape), a system that utilises proxy representations, such as silhouettes and 2D joints, as inputs to a shape and pose regression neural network, which is trained with synthetic training data (generated on-the-fly during training using the SMPL statistical body model) to overcome data scarcity.

AGORA: Avatars in Geography Optimized for Regression Analysis

pixelite1201/agora_evaluation CVPR 2021

Additionally, we fine-tune methods on AGORA and show improved performance on both AGORA and 3DPW, confirming the realism of the dataset.

Body Meshes as Points

jfzhang95/BMP CVPR 2021

In this work, we present a single-stage model, Body Meshes as Points (BMP), to simplify the pipeline and lift both efficiency and performance.

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

iGame-Lab/LASOR 1 Aug 2021

The lack of diverse and accurate pose and shape training data becomes a major bottleneck, especially for scenes with occlusions in the wild.