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

2026-07-22

AI Summary

This work addresses the challenges of native 2K-resolution virtual try-on with multiple garments, where high resolution leads to prohibitive memory consumption and diffusion models tend to oversmooth fine fabric details. To overcome these limitations, the authors propose an end-to-end, mask-free generative framework featuring three key innovations: an Adaptive Token Packing (ATP) mechanism that dynamically compresses sequence length in 2D feature space, a Multi-dimensional Try-on Reward (MTR) system that jointly optimizes texture fidelity and physical plausibility, and WearWow-2K—the first native 2K-resolution triplet dataset for multi-garment try-on. The proposed method substantially reduces memory overhead while preserving high-frequency textile details, achieving state-of-the-art performance in multi-clothing synthesis and outperforming existing commercial baselines.

Abstract

Synthesizing native 2K multi-garment virtual try-on is a formidable frontier in digital fashion, critically bottlenecked by two fundamental limitations: the O(N^2) memory explosion induced by 2k conditions, and the spectral bias of diffusion models that over-smooths high-frequency fabric details. We present WearWow, an end-to-end, mask-free generative framework that pioneers ultra-high-resolution multi-garment synthesis. To mitigate the memory explosion , we propose Adaptive 2D Token Packing (ATP). ATP leverages inherent garment sparsity to algorithmically pack heterogeneous items onto a unified 2D canvas and prune uninformative background tokens, minimizing the effective sequence length and subsequent memory overhead while rigorously preserving 2D spatial priors. To rectify texture degradation, we introduce the Multi-dimensional Try-on Reward (MTR) system. MTR synergizes a Semantic Guidance Reward to explicitly drive tactile restoration with a Cloth 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 comprising native 2K triplets, providing physically correct spatial interactions that naturally empower the model's mask-free generation. Extensive experiments demonstrate that WearWow establishes a new state-of-the-art, exceeding existing commercial baselines in native 2K multi-garment synthesis.

Problem

• virtual try-on
• 2K resolution
• memory explosion
• spectral bias
• multi-garment synthesis

Innovation

• Adaptive 2D Token Packing
• Multi-dimensional Try-on Reward
• native 2K virtual try-on
• mask-free generation
• diffusion model spectral bias