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Taobao’s AI virtual‑try‑on system pre‑computes fitting results offline, writes them into the Item Center via scalable ScheduleX tasks, optimizes pagination, locking and flow‑control, and thereby processes millions of apparel items in under thirty minutes with 99.9% success and reliable checkpoint‑resume monitoring.
This study evaluates virtual clothing try‑on by fine‑tuning LoRA models on a handful of garment images and integrating Stable Diffusion with ControlNet, Inpainting, and Segment Anything, showing that the AI‑generated pipeline matches or exceeds traditional geometric‑matching VTON in visual fidelity, especially for upper‑body apparel, while running locally on an RTX 3060.
Alibaba’s Creative & Video Platform showcases six newly accepted ACM MM and ECCV papers that introduce self‑supervised text‑erasing, a confidence‑driven action‑proposal module, a geometry‑aligned variational transformer for image‑conditioned layouts, a high‑resolution virtual‑try‑on system, a motion‑transformer for unsupervised animation, and a cross‑domain motion‑transfer framework, highlighting cutting‑edge AI for automated creative design, video editing, and e‑commerce applications.
The paper presents Deep Generative Projection (DGP), a virtual‑try‑on system that learns a realistic dressing distribution from unpaired images with StyleGAN, projects coarse garment‑human alignments into its latent space, refines details, and achieves higher fidelity and robustness than supervised SOTA methods without needing paired data.