SATURN: Autoregressive Image Generation Guided by Scene Graphs
This addresses the issue of capturing complex layouts and object relationships in image generation for applications requiring structural accuracy, though it is incremental as it builds on existing autoregressive architectures.
The paper tackled the problem of text-to-image models struggling with layout and object relationships by introducing SATURN, a lightweight extension to VAR-CLIP that uses scene graphs to guide autoregressive image generation, resulting in reduced FID from 56.45% to 21.62% and increased Inception Score from 16.03 to 24.78 on the Visual Genome dataset.
State-of-the-art text-to-image models excel at photorealistic rendering but often struggle to capture the layout and object relationships implied by complex prompts. Scene graphs provide a natural structural prior, yet previous graph-guided approaches have typically relied on heavy GAN or diffusion pipelines, which lag behind modern autoregressive architectures in both speed and fidelity. We introduce SATURN (Structured Arrangement of Triplets for Unified Rendering Networks), a lightweight extension to VAR-CLIP that translates a scene graph into a salience-ordered token sequence, enabling a frozen CLIP-VQ-VAE backbone to interpret graph structure while fine-tuning only the VAR transformer. On the Visual Genome dataset, SATURN reduces FID from 56.45% to 21.62% and increases the Inception Score from 16.03 to 24.78, outperforming prior methods such as SG2IM and SGDiff without requiring extra modules or multi-stage training. Qualitative results further confirm improvements in object count fidelity and spatial relation accuracy, showing that SATURN effectively combines structural awareness with state-of-the-art autoregressive fidelity.