3D Human Reconstruction
56 papers with code • 10 benchmarks • 15 datasets
Most implemented papers
PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization
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
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).
Perceiving 3D Human-Object Spatial Arrangements from a Single Image in the Wild
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.
Nerfies: Deformable Neural Radiance Fields
We present the first method capable of photorealistically reconstructing deformable scenes using photos/videos captured casually from mobile phones.
PyMAF: 3D Human Pose and Shape Regression with Pyramidal Mesh Alignment Feedback Loop
Regression-based methods have recently shown promising results in reconstructing human meshes from monocular images.
SCANimate: Weakly Supervised Learning of Skinned Clothed Avatar Networks
We present SCANimate, an end-to-end trainable framework that takes raw 3D scans of a clothed human and turns them into an animatable avatar.
ICON: Implicit Clothed humans Obtained from Normals
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
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.
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.
Self-supervised Learning of Motion Capture
In this work, we propose a learning based motion capture model for single camera input.