EVTAR: End-to-End Try on with Additional Unpaired Visual Reference
We propose EVTAR, an End-to-End Virtual Try-on model with Additional
Reference, that directly fits the target garment onto the person image while
incorporating reference images to enhance try-on accuracy. Most existing
virtual try-on approaches rely on complex inputs such as agnostic person
images, human pose, densepose, or body keypoints, making them labor-intensive
and impractical for real-world applications. In contrast, EVTAR adopts a
two-stage training strategy, enabling simple inference with only the source
image and the target garment inputs. Our model generates try-on results without
masks, densepose, or segmentation maps. Moreover, EVTAR leverages additional
reference images of different individuals wearing the same clothes to preserve
garment texture and fine-grained details better. This mechanism is analogous to
how humans consider reference models when choosing outfits, thereby simulating
a more realistic and high-quality dressing effect. We enrich the training data
with supplementary references and unpaired person images to support these
capabilities. We evaluate EVTAR on two widely used benchmarks and diverse
tasks, and the results consistently validate the effectiveness of our approach.
69097488812eca10f9cc61bb
Liuzhuozheng Li
claimed_verified
2025-11-04T20:26:36.730Z
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/avatars/14bb9a7bd887e358a3a447ea2847e07a.svg
liuzhuozheng Li
Jyugatsu
user
Jyugatsu
69097488812eca10f9cc61bc
Yue Gong
69097488812eca10f9cc61bd
Shanyuan Liu
claimed_verified
2025-11-10T09:32:42.943Z
638de818ded2c11511f5db22
/avatars/5795283123829a42e0687df3b4f5689c.svg
lsy
liushanyuan18
user
liushanyuan18
69097488812eca10f9cc61be
Bo Cheng
69097488812eca10f9cc61bf
Yuhang Ma
69097488812eca10f9cc61c0
Liebucha Wu
69097488812eca10f9cc61c1
Dengyang Jiang
claimed_verified
2025-12-02T08:50:21.436Z
662a0f2d4bab737c1a279843
https://cdn-avatars.huggingface.co/v1/production/uploads/662a0f2d4bab737c1a279843/fC2p3mjMHkVpDQdEqkuR4.png
Dengyang Jiang
DyJiang
user
DyJiang
69097488812eca10f9cc61c2
Zanyi Wang
69097488812eca10f9cc61c3
Dawei Leng
69097488812eca10f9cc61c4
Yuhui Yin
EVTAR: End-to-End Try on with Additional Unpaired Visual Reference
We propose EVTAR, an End-to-End Virtual Try-on model with Additional
Reference, that directly fits the target garment onto the person image while
incorporating reference images to enhance try-on accuracy. Most existing
virtual try-on approaches rely on complex inputs such as agnostic person
images, human pose, densepose, or body keypoints, making them labor-intensive
and impractical for real-world applications. In contrast, EVTAR adopts a
two-stage training strategy, enabling simple inference with only the source
image and the target garment inputs. Our model generates try-on results without
masks, densepose, or segmentation maps. Moreover, EVTAR leverages additional
reference images of different individuals wearing the same clothes to preserve
garment texture and fine-grained details better. This mechanism is analogous to
how humans consider reference models when choosing outfits, thereby simulating
a more realistic and high-quality dressing effect. We enrich the training data
with supplementary references and unpaired person images to support these
capabilities. We evaluate EVTAR on two widely used benchmarks and diverse
tasks, and the results consistently validate the effectiveness of our approach.
EVTAR is an end-to-end virtual try-on model that enhances accuracy by using reference images, simplifying the inference process and improving garment texture and detail preservation.
End-to-End Virtual Try-on agnostic person images human pose densepose body keypoints two-stage training strategy garment texture fine-grained details supplementary references unpaired person images