How to Implement AvatarPoser: A Full-Body Pose Tracking Tutorial

In the world of Mixed Reality, accurately representing a user’s full-body movements with avatars is vital for enhancing user experience. Traditional systems managing only upper body tracking can feel limiting and lead to unusual avatar representations. Fortunately, the innovative AvatarPoser allows you to track full-body poses using minimal input—specifically just head and hand movements. Let’s take a dive into how you can utilize AvatarPoser for your projects!

Getting Started with AvatarPoser

To successfully implement AvatarPoser, you need to follow a series of structured steps including data preparation, training, testing, and utilizing pre-trained models.

1. Datasets

The first step is to gather the necessary datasets. Here’s how:

• Download the datasets BMLrub, CMU, and HDM05 from AMASS.
• Get the required body model and place it in the support_data/body_models directory of your repository.
• For the SMPL+H body model, download it from this link. Make sure to use the AMASS version with DMPL blendshapes.
• If you need a new random data split, run generate_split.py.
• Use prepare_data.py to preprocess the input data for faster training. Note that the data split used in the paper is stored under the data_split folder.

2. Training

Once your data is ready, it’s time to train the model. Open your terminal and run:

python main_train_avatarposer.py -opt options/train_avatarposer.json

3. Testing

After training, you can evaluate the model. To do this, simply run:

python main_test_avatarposer.py

4. Pretrained Models

If you prefer to skip the training phase, you can utilize pre-trained models. Click Pretrained Models to download AvatarPoser’s pre-trained model and place it into the model_zoo directory.

Understanding the Code: An Analogy

To understand how AvatarPoser functions, imagine a conductor leading an orchestra. The head and hands of the user are like instruments providing input – the conductor (AvatarPoser) interprets these cues and orchestrates the entire body movement accordingly. Through a method called Transformer encoding, AvatarPoser decouples the global movements (like directions from the conductor) from the local joint movements (individual instrument performances), ensuring accurate full-body representation. Think of the inverse kinematics used in refining positions as the fine-tuning of volume and tempo of each instrument, giving the performance a unified feel.

Troubleshooting

If you run into issues during any of these steps, here are some troubleshooting tips to help you out:

• Ensure all necessary packages and dependencies are correctly installed and updated.
• Double-check that your dataset paths are accurately set up.
• If you encounter errors related to data splits, revisit the preprocessing step to confirm it completed successfully.
• For any performance issues, check if your hardware meets the minimum requirements for running the model.

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Conclusion

By following these steps and understanding the functionality behind AvatarPoser, you can significantly enhance your Mixed Reality projects. Remember that the accuracy of avatar representation not only improves user interaction but also enriches the overall experience within virtual environments. 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.