Scaling Action Recognition Models with Synthetic Data

Scaling Action Recognition Models with Synthetic Data: A Game-Changer for Computer Vision

Summary: Action recognition models are crucial for identifying and classifying human actions in various scenarios. However, developing robust models that can accurately recognize a wide range of actions across different domains remains challenging due to the lack of sufficient and diverse training data. Synthetic data generation (SDG) emerges as a practical solution to this issue by simulating real-world scenarios through 3D simulations. This article explores how NVIDIA leverages synthetic data to enhance the capabilities of action recognition models, highlighting the benefits and applications across industries.

The Challenge of Action Recognition

Action recognition models are designed to identify and classify human actions, such as walking or waving. However, developing robust models that can accurately recognize a wide range of actions across various scenarios remains challenging. One of the key hurdles is acquiring sufficient and diverse training data. Real-world data collection can be time-consuming and expensive, making it impractical for many use cases.

Synthetic Data Generation: A Practical Solution

Synthetic data generation (SDG) is the process of creating artificial data from physically accurate 3D simulations that mimic real-world data. This approach is particularly valuable in scenarios where gathering real-world data is costly or impractical. SDG can be used to create large-scale datasets for action recognition models, enabling the models to evolve efficiently through iterative training.

NVIDIA Isaac Sim: A Powerful Tool for Synthetic Data Generation

NVIDIA Isaac Sim is a reference application built on NVIDIA Omniverse for simulating and validating robots. It is utilized across multiple domains, including retail, sports, warehouses, and hospitals. Isaac Sim plays a crucial role in generating synthetic data for action recognition models. The process involves creating artificial data from 3D simulations that mimic real-world data, enabling the models to evolve efficiently through iterative training.

Creating a Human Action Recognition Dataset with Isaac Sim

To create a human action recognition dataset with Isaac Sim, you need to start with actions such as picking up an apple. From these actions, you can extract key points, which serve as inputs for the action recognition model. You can obtain action animations from any third-party vendor or create these animations using real videos.

Omni.Replicator.Agent (ORA), an Isaac Sim extension, is designed to generate synthetic data on human characters and robots across a variety of 3D environments. The ORA extension offers several features, including:

• Multi-camera consistency
• Multi-sensor logging
• Custom DataWriter support (skeletal data, 2D position, and segmentation)
• Position and orientation randomization for characters, agents, and objects

Training an Action Recognition Model with Synthetic Data

The synthetic data generated by Isaac Sim can be used to expand the capabilities of a spatial-temporal graph convolutional network (ST-GCN) model, a machine learning model that detects human actions based on skeletal information. In this example, the PoseClassificationNet model (ST-GCN architecture) is trained on top of the 3D skeleton data produced by Isaac Sim with NVIDIA TAO, a framework for efficiently training and fine-tuning ML models.

Experimental Results

The ST-GCN model trained solely on synthetic data achieved an impressive average accuracy across 85 action classes. This performance was further validated using the NTU-RGB+D dataset, demonstrating that the model could generalize well even when applied to real-world data it was not explicitly trained on.

Scaling and Orchestrating Data Generation

NVIDIA has also explored the use of NVIDIA OSMO, a cloud-native orchestration platform, to scale the data generation process. This has significantly accelerated data generation, allowing for the creation of thousands of samples with diverse action animations and camera angles.

Conclusion

Synthetic data generation is a game-changer for action recognition models, enabling the creation of large-scale datasets that can be used to train robust models. NVIDIA Isaac Sim is a powerful tool for generating synthetic data, offering a range of features that make it easy to create and customize datasets for action recognition models. By leveraging synthetic data, developers can create more accurate and efficient action recognition models that can be applied across various industries. Whether you’re working in retail, sports, or healthcare, synthetic data generation can help you take your action recognition models to the next level.