用统一模型生成搭配协调的时尚穿搭，效果领先。
Fashion Outfit Generation via Unified Sequential Composition Models
- 将穿搭生成建模为带约束的序列生成问题，联合建模搭配兼容性和组合意图。
- 在Polyvore数据集上超越现有方法，在人工评估和自动指标上均表现最佳。
- 适合时尚生成、个性化推荐与创意设计领域的研究者与工程师。
从海量服装单品中生成风格一致的时尚穿搭是一项挑战，主要源于美学兼容性的非单调性和隐含性，以及组合空间的指数级增长。本文将该任务形式化为受限集合生成（CEG），并建模为有限时域确定性马尔可夫决策过程。提出统一序列组合模型（USCM），联合建模集合级兼容性与潜在组合意图。基于USCM学习到的先验，设计潜空间扩展蒙特卡洛树搜索（LE-MCTS）机制，用于生成过程中的单品检索，平衡局部审美协同与全局结构均衡。在Polyvore Outfits数据集上的大量实验，以及在iFashion和PolyvoreU数据集上的零样本评估表明，本框架在独立的人工偏好评价、自动化美学代理指标和结构有效性度量上均达到当前最优性能。
原文摘要 · Abstract (English)
The task of synthesizing stylistically coherent fashion outfits from massive item libraries, known as fashion outfit generation, remains a non-trivial challenge, primarily due to the non-monotonic and implicit nature of aesthetic compatibility, coupled with the exponentially large combinatorial search space. In this paper, we formalize this task as Constrained Ensemble Generation (CEG) and model it as a finite-horizon deterministic Markov Decision Process. To address CEG in fashion, we propose the Unified Sequential Composition Model (USCM), which jointly models set-level compatibility and latent composition intents. Guided by USCM's learned priors, a Latent Expansion Monte Carlo Tree Search (LE-MCTS) mechanism is proposed to handle item retrieval during composition, balancing local aesthetic synergy with global structural balance. Extensive experiments on the Polyvore Outfits dataset, along with zero-shot evaluations on the iFashion and PolyvoreU datasets, demonstrate that our framework achieves state-of-the-art performance across independent human preference evaluations, automated aesthetic proxies, and structural validity metrics for constrained fashion outfit generation.
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