发表机构
Shenzhen Campus of Sun Yat-sen University; Meitu Lab（中山大学深圳校区; 美图实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对原生2K多服装虚拟试穿难题，提出WearWow框架。用自适应2D令牌打包减轻内存爆炸，多维试穿奖励系统纠正纹理退化，还构建高质量数据集。实验证明该框架在原生2K多服装合成上达新水平，超越现有商业基线。
AI 中文摘要
合成原生2K多服装虚拟试穿是数字时尚领域的一个艰巨前沿，受到两个基本限制的严重瓶颈：2K条件导致的O(N^2)内存爆炸，以及扩散模型的频谱偏差导致高频织物细节过度平滑。我们提出了WearWow，一个端到端、无掩码的生成框架，开创了超高分辨率多服装合成的先河。为了减轻内存爆炸，我们提出了自适应2D令牌打包(ATP)。为了纠正纹理退化，我们引入了多维试穿奖励(MTR)系统。此外，我们策划了WearWow-2K，一个包含原生2K三元组的极端质量数据集。广泛的实验表明，WearWow建立了新的技术水平，在原生2K多服装合成方面超过了现有的商业基线。
英文摘要
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.
