Lab 1: SMPL-X Human Body Modeling | Yixin Zhu

Initial Codebase for Gradescope Submission

Background

This assignment serves as a comprehensive recap of an important topic covered in our course: human body representation using SMPL-X. You will work with 3D human pose visualization and joint-to-smplx optimization that are widely used in computer vision and graphics research.

SMPL-X (SMPL eXpressive) is a expressive body model that represents the human body shape and pose through a parametric model. It extends the original SMPL model to include facial expressions and hand articulation, making it suitable for full-body human modeling applications.

In this assignment, you will implement key components for this topic, demonstrating your understanding of 3D human modeling.

Learning Objectives

By completing this assignment, you will:

Understand the SMPL-X parametric human body model
Gain hands-on experience with PyTorch and 3D graphics libraries

Environment Setup

Prerequisites

Python 3.8 or higher
Linux environment (recommended)
CUDA-capable GPU (recommended)

Installation

Unzip and navigate to the lab directory

Create and activate a conda virtual environment:

conda create -n core_lab1 python=3.8
conda activate core_lab1

Install Required Packages
```
pip3 install -r requirements.txt
```

Verify Installation:

python -c "import torch; import smplx; import trimesh; print('Environment setup successful')"

Assignment Tasks

Task 1: SMPL-X Pose Visualization (50 points)

Objective: Implement a function to visualize 3D human poses using the SMPL-X model.

Description: You will complete the visualize_smplx() function in framework.py. Given SMPL-X parameters (translation, body pose, and global orientation), your function should generate and display a 3D mesh visualization of the human pose and save it in .png format.

Key Requirements

Load the SMPL-X model correctly

Expected Output

3D mesh visualization saved as image .png file

Task 2: Joints to SMPL-X Pose Regression (50 points)

Objective: Implement an optimization function to estimate SMPL-X parameters from 3D joint positions.

Description: Sometimes you can only get the 3D joint positions of a human pose, therefore you need to convert the joint positions to SMPL-X parameters. Complete the optimize_smplx() function that takes 3D joint positions as input and optimizes SMPL-X parameters to best fit these target joints. This involves setting up an optimization loop with appropriate loss functions.

Key Requirements

Define appropriate loss function (joint position error)
Implement gradient-based optimization using PyTorch
Return optimized SMPL-X parameters

Expected Output

Optimized SMPL-X parameters (translation, body_pose, global_orient)
Final loss value (about 8e-5 after 100 iterations)
You should achieve a similar mesh rendering result as in Task 1

Implementation Guidelines

Code Structure

Only modify code within ## TODO sections in framework.py
Do not change function signatures or import statements
Maintain consistent tensor shapes and data types

Debugging Tips

Use breakpoint() to check intermediate results
Visualize intermediate results when possible

Hand-in Requirements

Submission Format

Submit only one file: framework.py
Platform: Gradescope autograder

File Requirements

Your framework.py must contain all implemented functions
Do not modify function signatures or imports
Include necessary comments explaining your implementation approach

Autograder Testing

Your submission will be tested against multiple test cases
Partial credit will be awarded for partially correct implementations
Timeout limit: 10 minutes in total

Academic Integrity

This is an individual assignment. While you may discuss general concepts with classmates, all submitted code must be your own work. Plagiarism will result in course failure.

Allowed

Discussing assignment requirements and clarifications
Sharing debugging strategies (without code)
Using official documentation and course materials

Not Allowed

Sharing or copying code solutions
Using external implementations without permission
Collaborating on actual implementation

Additional Resources

Documentation

Good luck with your assignment!