3D Human Shape Estimation
18 papers with code • 2 benchmarks • 7 datasets
AGORA
BEDLAM
SSP-3D
MoVi
HUMAN4D
HBW
UNIPD-BPE
Most implemented papers
End-to-end Recovery of Human Shape and Pose
open-mmlab/mmpose
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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
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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
First, ICON infers detailed clothed-human normals (front/back) conditioned on the SMPL(-X) normals.
AnthroNet: Conditional Generation of Humans via Anthropometrics
We present a novel human body model formulated by an extensive set of anthropocentric measurements, which is capable of generating a wide range of human body shapes and poses.
Learning to Reconstruct 3D Human Pose and Shape via Model-fitting in the Loop
nkolot/SPIN
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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
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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
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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
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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
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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
In this work, we present a single-stage model, Body Meshes as Points (BMP), to simplify the pipeline and lift both efficiency and performance.
