Synthetic Object

Synthetic object research focuses on generating realistic virtual objects and scenes for various applications, primarily driven by the need for diverse and high-quality training data for computer vision and related fields. Current research emphasizes leveraging generative models, particularly diffusion models, to create highly realistic synthetic objects with diverse textures, materials, and lighting conditions, and incorporating these into inverse rendering pipelines for improved relighting and view synthesis. This work is crucial for advancing areas like autonomous driving, deepfake detection, and 3D modeling, where access to large, varied real-world datasets is often limited or impractical.