VPBRANDLOOP: AIGC ADVERTISING IMAGE GENERATION WITH CROSS-SCENE VANISHING-POINT CONSTRAINTS

Computational advertising requires scalable creative generation that preserves scene structure and brand identity while supporting reliable pre-deployment quality control. We present VPBrandLoop (Vanishing-Point and Brand-conditioned Closed-Loop Advertising Generation), a framework for multi-scene AIGC advertising image generation that addresses perspective drift, under-utilized visualbrand conditions, and the absence of verifiable output-side geometric control. A precomputed dominant vanishing point (VP) and perspective attributes are injected into a latent diffusion model through multi-scale geometric residuals, while product and logo references are encoded as visual brand tokens and fused through decoupled cross-attention. Training uses brand-region-weighted diffusion reconstruction, spatial-gating alignment, and auxiliary brand-discriminative supervision. At inference time, a line-segment detector (LSD) and RANSAC re-estimate the dominant VP of each generated candidate; spatial-consistency quality gating and confidence-weighted geometric feedback then drive candidate resampling. We additionally use a CLIP-based zero-shot protocol to evaluate the multimodal semantic retrievability of brands, product categories, and visually observable attributes. On 1,000 AdsGeo-10K test images evaluated across 12 methods, VPBrandLoop achieves an N-VPE of 0.2558 and an SCPR of 16.9%. Relative to ControlNet,the strongest external baseline on the corresponding metrics, N-VPE decreases by 20.3% and SCPR increases by 4.2 percentage points; both paired differences remain significant after Holm correction. The evidence therefore supports a primary advantage in preserving dominant perspective consistency between the input scene and the generated advertisement.