WearWow: Native 2K Multi-Garment Virtual Try-On via Adaptive Token Packing and Preference Alignment

Synthesizing native 2K multi-garment virtual try-on is aformidable frontier in digital fashion, critically bottlenecked by two fun-damental limitations: the O(N 2 ) memory explosion induced by 2k con-ditions, and the spectral bias of diffusion models that over-smooths high-frequency fabric details. We present WearWow, an end-to-end, mask-freegenerative framework that pioneers ultra-high-resolution multi-garmentsynthesis. To mitigate the memory explosion , we propose Adaptive 2DToken Packing (ATP). ATP leverages inherent garment sparsity to al-gorithmically pack heterogeneous items onto a unified 2D canvas andprune uninformative background tokens, minimizing the effective se-quence length and subsequent memory overhead while rigorously pre-serving 2D spatial priors. To rectify texture degradation, we introducethe Multi-dimensional Try-on Reward (MTR) system. MTR synergizes aSemantic Guidance Reward to explicitly drive tactile restoration with aCloth Distribution Reward to implicitly anchor the physical distribution,a joint formulation that effectively mitigates the severe reward hacking.Furthermore, we curate WearWow-2K, an extreme-quality dataset com-prising native 2K triplets, providing physically correct spatial interac-tions that naturally empower the model’s mask-free generation. Exten-sive experiments demonstrate that WearWow establishes a new state-of-the-art, exceeding existing commercial baselines in native 2K multi-garment synthesis.