This paper presents an overview of the inaugural PortraitCraft Challenge, held as one of the official competitions at CVPR 2026. The challenge focuses on portrait composition understanding and generation, aiming to advance AI research in portrait aesthetics analysis and controllable image synthesis. Unlike existing datasets and tasks that primarily focus on global aesthetic scoring, PortraitCraft introduces a unified evaluation framework comprising two complementary tracks. Track 1 requires models to perform structured portrait composition understanding, and Track 2 requires models to generate portrait images from structured composition descriptions under explicit compositional constraints. To support the challenge, we constructed and publicly released a large-scale portrait composition dataset consisting of approximately 50,000 curated real portrait images, providing multi-level supervision. This report describes the challenge setup, evaluation protocols, dataset composition, and final results, along with an analysis of the technical characteristics of the submitted solutions. The PortraitCraft Challenge provides a standardized and reproducible platform for research on portrait composition understanding and generation, and is expected to foster further progress in the fields of portrait aesthetics and controllable image generation.

翻译：本文概述了首届PortraitCraft挑战赛，该赛事作为CVPR 2026官方竞赛之一举行。挑战赛聚焦于肖像构图理解与生成，旨在推动AI在肖像美学分析与可控图像合成领域的研究。与主要关注全局美学评分的现有数据集和任务不同，PortraitCraft引入了一个包含两个互补赛道的统一评估框架。赛道一要求模型执行结构化肖像构图理解，赛道二则要求模型根据结构化构图描述，在明确的构图约束下生成肖像图像。为支持本次挑战赛，我们构建并公开发布了大规模肖像构图数据集，包含约50,000张精心挑选的真实肖像图像，提供多层级标注。本报告描述了挑战赛的设置、评估协议、数据集组成和最终结果，并对提交方案的技术特点进行了分析。PortraitCraft挑战赛为肖像构图理解与生成研究提供了一个标准化、可复现的平台，预计将推动肖像美学与可控图像生成领域的进一步发展。