3D Multi-Person Mesh Recovery

9 papers with code • 2 benchmarks • 2 datasets

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Most implemented papers

Monocular, One-stage, Regression of Multiple 3D People

Arthur151/ROMP ICCV 2021

Through a body-center-guided sampling process, the body mesh parameters of all people in the image are easily extracted from the Mesh Parameter map.

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.

Monocular Expressive Body Regression through Body-Driven Attention

vchoutas/expose ECCV 2020

To understand how people look, interact, or perform tasks, we need to quickly and accurately capture their 3D body, face, and hands together from an RGB image.

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.

Collaborative Regression of Expressive Bodies using Moderation

YadiraF/PIXIE 11 May 2021

Second, human shape is highly correlated with gender, but existing work ignores this.

Body Size and Depth Disambiguation in Multi-Person Reconstruction from Single Images

nicolasugrinovic/size_depth_disambiguation 2 Nov 2021

We address the problem of multi-person 3D body pose and shape estimation from a single image.

One-Stage 3D Whole-Body Mesh Recovery with Component Aware Transformer

IDEA-Research/OSX CVPR 2023

It is challenging to perform this task with a single network due to resolution issues, i. e., the face and hands are usually located in extremely small regions.

Reconstructing Groups of People with Hypergraph Relational Reasoning

boycehbz/GroupRec ICCV 2023

To address the obstacles, we fully exploit crowd features for reconstructing groups of people from a monocular image.

SMPLer-X: Scaling Up Expressive Human Pose and Shape Estimation

caizhongang/SMPLer-X NeurIPS 2023

1) For the data scaling, we perform a systematic investigation on 32 EHPS datasets, including a wide range of scenarios that a model trained on any single dataset cannot handle.