3D Human Reconstruction
49 papers with code • 8 benchmarks • 13 datasets
Benchmarks
These leaderboards are used to track progress in 3D Human Reconstruction
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AGORA
BEHAVE
Dynamic FAUST
SSP-3D
MoVi
CAPE
HBW
X-Humans
Most implemented papers
Expressive Body Capture: 3D Hands, Face, and Body from a Single Image
vchoutas/smplify-x
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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
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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
To our knowledge, this is the first generative model that directly dresses 3D human body meshes and generalizes to different poses.
Learning 3D Human Shape and Pose from Dense Body Parts
HongwenZhang/DaNet-DensePose2SMPL
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Reconstructing 3D human shape and pose from monocular images is challenging despite the promising results achieved by the most recent learning-based methods.
TetraTSDF: 3D human reconstruction from a single image with a tetrahedral outer shell
diegothomas/TetraTSDF
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CVPR 2020
In this paper, we propose the tetrahedral outer shell volumetric truncated signed distance function (TetraTSDF) model for the human body, and its corresponding part connection network (PCN) for 3D human body shape regression.
Coherent Reconstruction of Multiple Humans from a Single Image
JiangWenPL/multiperson
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CVPR 2020
Our goal is to train a single network that learns to avoid these problems and generate a coherent 3D reconstruction of all the humans in the scene.
PaMIR: Parametric Model-Conditioned Implicit Representation for Image-based Human Reconstruction
To overcome the limitations of regular 3D representations, we propose Parametric Model-Conditioned Implicit Representation (PaMIR), which combines the parametric body model with the free-form deep implicit function.
Combining Implicit Function Learning and Parametric Models for 3D Human Reconstruction
In this work, we present methodology that combines detail-rich implicit functions and parametric representations in order to reconstruct 3D models of people that remain controllable and accurate even in the presence of clothing.
Perceiving 3D Human-Object Spatial Arrangements from a Single Image in the Wild
facebookresearch/phosa
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ECCV 2020
We present a method that infers spatial arrangements and shapes of humans and objects in a globally consistent 3D scene, all from a single image in-the-wild captured in an uncontrolled environment.
NormalGAN: Learning Detailed 3D Human from a Single RGB-D Image
LizhenWangT/NormalGAN
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ECCV 2020
We propose NormalGAN, a fast adversarial learning-based method to reconstruct the complete and detailed 3D human from a single RGB-D image.
