The presentation discusses the use of synthetic data and neural networks to generate high-quality labels and diverse datasets for training models. The focus is on assigning a latent code to each asset to represent a family of objects.
- Synthetic data can provide high-quality labels on a pixel level that is impossible to achieve with real data
- Variety in datasets is important for training models
- Assets are generated by 3D artists using a dedicated software and encoded with a family of assets
- Assigning a latent code to each asset can represent a family of objects
- The network is incentivized to decompose information in the latent code