Vanast: Virtual Try-On with Human Image Animation via Synthetic Triplet Supervision
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.
69d5c34fb9b236b710ae817f
Hyunsoo Cha
claimed_verified
2026-04-08T08:04:44.449Z
62414e2b585605a4079c2f38
/avatars/db1dc5dd2164b7ecbd789104329296bd.svg
Hyunsoo Cha
HyunsooCha
user
HyunsooCha
69d5c34fb9b236b710ae8180
Wonjung Woo
69d5c34fb9b236b710ae8181
Byungjun Kim
69d5c34fb9b236b710ae8182
Hanbyul Joo
Vanast: Virtual Try-On with Human Image Animation via Synthetic Triplet Supervision
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.
Vanast is a unified framework that generates garment-transferred human animation videos by combining image-based virtual try-on and pose-driven animation in a single process, addressing issues like identity drift and garment distortion through triplet supervision and dual module architecture.
virtual try-on pose-driven animation garment transfer video diffusion transformers dual module architecture triplet supervision identity preservation garment accuracy pose adherence