Welcome to the world of ICON, where we take a bold leap into the future of 3D reconstruction! In this blog post, we will walk you through the process of using ICON, an innovative project that reconstructs 3D human forms from RGB images with clothing. Whether you’re a researcher eager to evaluate cutting-edge technology or an enthusiast diving into AI development, you’re in the right place!

Who Needs ICON?

ICON is suitable for individuals looking to train and evaluate models using PIFu, PaMIR, and ICON itself with their own datasets. If you’re curious about the potential outputs ICON can generate from a raw RGB image, here’s what you can expect:

• Image (PNG):
  • Segmented human RGB
  • Normal maps of both body and clothing
  • Pixel-aligned normal-RGB overlap
• Mesh (OBJ):
  • SMPL-(X) body from various sources like PyMAF, PIXIE, PARE, HybrIK, BEV
  • 3D clothed human reconstruction
  • 3D garments (requires a 2D mask)
• Video (MP4):
  • Self-rotated clothed human

Instructions

Let’s get started with setting up ICON! Follow these steps:

• Install all the required packages and set up the models by visiting the installation guide.
• Synthesize the training, validation, and test dataset from THuman2.0 following the instructions in the dataset documentation.
• Train your model using THuman2.0 by reviewing the training documentation.
• Benchmark your trained models on the CAPE test set as described in the evaluation documentation.
• As an add-on, you can check the guide on garment extraction from fashion images, developed by ETH Zürich students.

Running Demo

To see ICON in action, follow these steps in your terminal:

bash
cd ICON
# Choose your model type:
# pifu            reimplemented PIFu
# pamir           reimplemented PaMIR
# icon-filter     ICON w global encoder (continuous local wrinkles)
# icon-nofilter   ICON w/o global encoder (correct global pose)
# icon-keypoint   ICON w relative-spatial encoding (insight from KeypointNeRF)

python -m apps.infer -cfg .configs/icon-filter.yaml -gpu 0 -in_dir .examples -out_dir .results -export_video -loop_smpl 100 -loop_cloth 200 -hps_type pixie

This command is like entering a secret door that reveals the full potential of your data! Each model type option will bring different refinements and features to explore.

Troubleshooting

If you run into issues or have questions while using ICON, here are some troubleshooting tips:

• Ensure all required dependencies are installed as mentioned in the installation guide. Missed dependencies can lead to unexpected errors.
• Check your dataset preparation meticulously; discrepancies in formats can break your evaluation scripts.
• If the results are unsatisfactory, consider experimenting with different model types as they cater to various aspects of the data.
• For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Final Thoughts

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.

Happy coding and exploring with ICON!