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arXiv · 2604.04934

Vanast: Virtual Try-On with Human Image Animation via Synthetic Triplet Supervision

Hyunsoo Cha·Wonjung Woo·Byungjun Kim·Hanbyul Joo
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Abstract

We present Vanast, a unified framework that generates garment-transferred human animation videos directly from a single human image, garment images, and a pose guidance video. Conventional two-stage pipelines treat image-based virtual try-on and pose-driven animation as separate processes, which often results in identity drift, garment distortion, and front-back inconsistency. Our model addresses these issues by performing the entire process in a single unified step to achieve coherent synthesis. To enable this setting, we construct large-scale triplet supervision. Our data generation pipeline includes generating identity-preserving human images in alternative outfits that differ from garment catalog images, capturing full upper and lower garment triplets to overcome the single-garment-posed video pair limitation, and assembling diverse in-the-wild triplets without requiring garment catalog images. We further introduce a Dual Module architecture for video diffusion transformers to stabilize training, preserve pretrained generative quality, and improve garment accuracy, pose adherence, and identity preservation while supporting zero-shot garment interpolation. Together, these contributions allow Vanast to produce high-fidelity, identity-consistent animation across a wide range of garment types.

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Hyunsoo Cha, Wonjung Woo, Byungjun Kim, Hanbyul Joo. 2026-04-06. Vanast: Virtual Try-On with Human Image Animation via Synthetic Triplet Supervision. https://arxiv.org/abs/2604.04934

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