CVPR 2023 - Efficient Scale-Invariant Generator with Column-Row Entangled Pixel Synthesis

In this episode we discuss Efficient Scale-Invariant Generator with Column-Row Entangled Pixel Synthesis by Thuan Hoang Nguyen, Thanh Van Le, Anh Tran. The paper proposes a new generative model called Column-Row Entangled Pixel Synthesis (CREPS) that can efficiently and scalably synthesize photo-realistic images of any arbitrary resolution. Existing GAN-based solutions suffer from inconsistency and texture sticking issues when scaling output resolution, while INR-based generators have a huge memory footprint and slow inference, making them unsuitable for large-scale or real-time systems. CREPS avoids these problems by using a novel bi-line representation that decomposes layer-wise feature maps into separate "thick" column and row encodings, enabling it to synthesize scale-consistent and alias-free images at any resolution with proper training and inference speed.