Animating Non-Rigid Neural Implicit Shapes with SNARF

Welcome to the exciting world of computer vision and animation! Today, we will explore how to use the innovative forward skinning technique proposed in the paper SNARF: Differentiable Forward Skinning for Animating Non-Rigid Neural Implicit Shapes. This approach allows us to animate neural implicit shapes with remarkable generalization to unseen poses.

Quick Start Guide

Getting started with SNARF involves a few straightforward steps:

• Clone the Repository: Open your terminal and run:

git clone https://github.com/xuchen-ethz/snarf.git
cd snarf

• Install the Environment: You will need to set up the required environment using conda:

conda env create -f environment.yml
conda activate snarf

• Download SMPL Models: You need to download SMPL models from this link and organize them in the right place.
• Download Pretrained Models: Use the provided shell script to download test motion sequences:

sh .download_data.sh

• Run a Quick Demo: Try a demo for clothed humans with the command:

python demo.py expname=cape subject=3375 demo.motion_path=data/aist_demo_seqs +experiments=cape

Explaining SNARF with an Analogy

Imagine you’re a skilled sculptor crafting a statue (our neural implicit shape) out of a block of marble, but instead of using a chisel, you’re animating this statue dynamically as you go. The forward skinning module is like the flexible scaffolding you build around your statue that allows for deformation while ensuring it retains its overall structure. Just like how scaffolding can bend and twist without losing its form, SNARF uses an intelligent mechanism to animate poses smoothly even when the sculpture deforms in surprising ways. This system enables the animation of a character in various poses without having to start from scratch each time, allowing for flexibility and creativity.

Training and Evaluation

To train your model, you’ll need to download specific datasets:

• Minimally Clothed Human:
  • Download the AMASS dataset, and unzip it into the data folder.
  • Run the preprocessing command for the datasets:

  python preprocess_sample_points.py --output_folder data/DFaust_processed

• Training the Model:
  • Run the following command to start training:

  python train.py subject=50002

Troubleshooting

If you encounter issues such as environment compatibility, ensure that you have the correct Python version installed. Additionally, verify that you’ve followed each installation step thoroughly.

To tackle specific errors, consider consulting the installation documentation of Kaolin and reviewing logs for clues on what might be going wrong. For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

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.

By following this guide, you should be well on your way to utilizing SNARF for animating non-rigid neural implicit shapes effectively. Dive in and explore the vast possibilities this technology offers!