Think building custom digital twins for AI training is hard? Let us show you how to make it easy!
Next week, Duality AI is offering a free "Creating Your Own 4-Wheeled Vehicle Digital Twins for AI Training with Falcon Editor" live class. Sign up here: https://forms.gle/2U5xugMjvSkZdeaR8
What we'll cover: 🚗 Import & Configure a rigged 4-wheeled vehicle and transform it into a controllable system twin using Blueprints. 🚗 Enable Dynamic Control by exposing Python variables for real-time adjustments. 🚗 Attach Sensors to capture valuable simulation data. 🚗 Assemble & Run a Simulation Scenario to generate training data for AI & robotics applications.
See how Falcon creates synthetic data for faster, easier, and more targeted AI training by creating a FREE account here: https://www.duality.ai/edu
Why train a model in pose estimation? Many functions require pose estimation, such as... 🤖Defining the orientation of an object on a conveyer belt for pick and place operations with a robotic arm, 🤖 Replicating an object's pose for AR and VR application overlays, 🤖 Localizing and mapping an environment for path navigation, 🤖 ...and more!
What makes this dataset useful for training a model?
💠Duality AI's digital twins are not generated by AI, but are instead crafted by 3D artists. This allows the training from this data to transfer into real-world applicability. Read more about our techniques for bridging the Sim2Real gap here: https://www.duality.ai/blog/ml-synthetic-data-model 💠The simulation software, called FalconEditor, can easily create thousands of images with varying lighting, posing, occlusions, backgrounds, camera positions, and more using a versatile python API 💠FalconEditor is able to create 100% accurate labels at the SAME TIME as it creates the images. This makes collecting data for tasks such pose-estimation easier, faster, and more reliable.
Interested in a specific dataset? Let us know! We're always looking for new ideas on what datasets to create next 👀
