Toward Spatially Unbiased Generative Models

Understand

Recent image generation models show remarkable generation performance.

• However, they mirror strong location preference in datasets, which we call spatial bias.
• Therefore, generators render poor samples at unseen locations and scales.
• We argue that the generators rely on their implicit positional encoding to render spatial content.