sellThesis sellDFKI sellAugmented Vision sell3D Vision sellHuman Mesh Recovery

Master's Thesis: Multi-Person Human Mesh Recovery with Anny

Muhammad Saif Ullah Khan

Summary

This thesis focuses on human mesh recovery (HMR): predicting a full 3D body surface from an image or a video. Instead of returning only a few body joints, an HMR system estimates a dense mesh, which is a 3D surface made of connected vertices.

Overview of the thesis pipeline
Train a system that can recover a full 3D human body mesh for multiple people from RGB input. The primary target is the Anny body model, where we want to recover both pose and Anny phenotype parameters (shape parameters) from RGB inputs.

The main research target is to train HMR for the Anny body model. Anny is an open and interpretable human body model designed to cover a wider range of ages and body shapes. A secondary goal is to study compatibility with SMPL-X / SMPL, which are widely used body models in 3D human analysis.

The project will mainly study the multi-person setting, where several people appear in the same image. A possible extension is video-based HMR, where the same person must be followed across frames using tracking.

TL;DR: Build a system that reconstructs full 3D human body meshes for multiple people from images or video. The main goal is to make this work well for the Anny body model. A secondary goal is compatibility with SMPL-X / SMPL. The thesis combines deep learning, 3D vision, datasets, and efficient model design.

Glossary

Term Description
Human mesh recovery (HMR) estimating a full 3D body mesh from RGB images or video.
Multi-person handling several people at the same time, not just one person.
Body model a parameterized template that converts pose and shape values into a 3D mesh.
Anny the main target model in this thesis.  
SMPL-X / SMPL established body models used as references or secondary outputs.
Tracking matching the same person across video frames.

Proposed research question

Can we train an accurate and efficient multi-person HMR system for the Anny body model, and can we keep the model size significantly smaller than a strong baseline while maintaining competitive accuracy?

Expected work

  1. Study recent literature on multi-person HMR and body models.
  2. Reproduce or adapt a strong baseline for multi-person HMR.
  3. Train the pipeline primarily for Anny.
  4. Use BEDLAM and Anny-One as starting datasets, and review whether additional datasets are needed.
  5. Evaluate accuracy, runtime, and model size.
  6. Optionally extend the system from single images to video by adding temporal consistency or tracking.

Main datasets and models

  • Anny / Anny-One for the primary body-model target.
  • BEDLAM for large-scale synthetic supervision with 3D body annotations.
  • SMPL-X / SMPL for comparison, interoperability, or secondary output support.
  • Multi-HMR as a likely starting reference for the multi-person setting.

Target outcome

A successful thesis should deliver:

  • a clean training and evaluation pipeline,
  • a model that works on multi-person RGB scenes,
  • primary support for Anny,
  • an analysis of accuracy vs. efficiency,
  • and, if time permits, a video extension with tracking.

A stretch goal is to reach roughly 50% of the parameter count of a strong baseline while keeping similar or better accuracy.

Who this thesis is for

This topic is suitable for a student who is comfortable with:

  • Python and PyTorch,
  • deep learning for computer vision,
  • reading recent research papers,
  • and implementing clean, reproducible experiments.

Useful starting references

Maintained by saifkhichi96 on GitHub.

The website is distributed under different open-source licenses. For more details, see the notice at the bottom of the page.