EVTAR: End-to-End Try on with Additional Unpaired Visual Reference
Authors: name: Liuzhuozheng Li, name: Yue Gong, name: Shanyuan Liu, name: Bo Cheng, name: Yuhang Ma, name: Liebucha Wu, name: Dengyang Jiang, name: Zanyi Wang, name: Dawei Leng, name: Yuhui Yin
Organization: 北京奇虎科技有限公司

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.