FidPoster: End-to-End Product Poster Generation with Fine-grained Product Detail Fidelity

Fine-grained Product Detail Fidelity refers to preserving a reference product’s packaging text, textures, and colors in generated posters. Existing models often distort these details, compromising commercial usability despite visually appealing designs. Our analysis shows that VAE reconstruction degrades fine-grained details, highlighting the need for direct supervision of decoded appearance. During sampling, the absence of explicit fidelity guidance can further allow product details to drift from the reference. These observations motivate FidPoster, an end-to-end framework for Fidelity-preserving product Poster generation that combines pixel-space fidelity learning with reference-guided trajectory correction. During training, Pixel-Space Fidelity Activation (PFA) directly supervises decoded product regions to align optimization with visible detail preservation. During inference, Energy-Guided Velocity Correction (EVC) uses bidirectional correspondence energy to measure inconsistent and missing details relative to the reference. Gradients of this energy correct the sampling velocity to suppress detail drift without requiring externally provided product masks at inference. We also construct FidPoster14K with 14K product-poster pairs and FidPoster-Bench, a 1,000-sample benchmark comprising an 800-sample Fine-grained Fidelity setting for controlled fidelity evaluation and a 200-sample Independent Reference setting for assessing generalization to independently sourced products. Experiments across both settings demonstrate consistent improvements in product-detail fidelity while maintaining competitive promotional-text accuracy and poster quality.