474,399 research outputs found
VITON: An Image-based Virtual Try-on Network
We present an image-based VIirtual Try-On Network (VITON) without using 3D
information in any form, which seamlessly transfers a desired clothing item
onto the corresponding region of a person using a coarse-to-fine strategy.
Conditioned upon a new clothing-agnostic yet descriptive person representation,
our framework first generates a coarse synthesized image with the target
clothing item overlaid on that same person in the same pose. We further enhance
the initial blurry clothing area with a refinement network. The network is
trained to learn how much detail to utilize from the target clothing item, and
where to apply to the person in order to synthesize a photo-realistic image in
which the target item deforms naturally with clear visual patterns. Experiments
on our newly collected Zalando dataset demonstrate its promise in the
image-based virtual try-on task over state-of-the-art generative models
Virtual Accessory Try-On via Keypoint Hallucination
The virtual try-on task refers to fitting the clothes from one image onto
another portrait image. In this paper, we focus on virtual accessory try-on,
which fits accessory (e.g., glasses, ties) onto a face or portrait image.
Unlike clothing try-on, which relies on human silhouette as guidance, accessory
try-on warps the accessory into an appropriate location and shape to generate a
plausible composite image. In contrast to previous try-on methods that treat
foreground (i.e., accessories) and background (i.e., human faces or bodies)
equally, we propose a background-oriented network to utilize the prior
knowledge of human bodies and accessories. Specifically, our approach learns
the human body priors and hallucinates the target locations of specified
foreground keypoints in the background. Then our approach will inject
foreground information with accessory priors into the background UNet. Based on
the hallucinated target locations, the warping parameters are calculated to
warp the foreground. Moreover, this background-oriented network can also easily
incorporate auxiliary human face/body semantic segmentation supervision to
further boost performance. Experiments conducted on STRAT dataset validate the
effectiveness of our proposed method
Single Stage Multi-Pose Virtual Try-On
Multi-pose virtual try-on (MPVTON) aims to fit a target garment onto a person
at a target pose. Compared to traditional virtual try-on (VTON) that fits the
garment but keeps the pose unchanged, MPVTON provides a better try-on
experience, but is also more challenging due to the dual garment and pose
editing objectives. Existing MPVTON methods adopt a pipeline comprising three
disjoint modules including a target semantic layout prediction module, a coarse
try-on image generator and a refinement try-on image generator. These models
are trained separately, leading to sub-optimal model training and
unsatisfactory results. In this paper, we propose a novel single stage model
for MPVTON. Key to our model is a parallel flow estimation module that predicts
the flow fields for both person and garment images conditioned on the target
pose. The predicted flows are subsequently used to warp the appearance feature
maps of the person and the garment images to construct a style map. The map is
then used to modulate the target pose's feature map for target try-on image
generation. With the parallel flow estimation design, our model can be trained
end-to-end in a single stage and is more computationally efficient, resulting
in new SOTA performance on existing MPVTON benchmarks. We further introduce
multi-task training and demonstrate that our model can also be applied for
traditional VTON and pose transfer tasks and achieve comparable performance to
SOTA specialized models on both tasks
Virtual Try-On With Generative Adversarial Networks: A Taxonomical Survey
This chapter elaborates on using generative adversarial networks (GAN) for virtual try-on applications. It presents the first comprehensive survey on this topic. Virtual try-on represents a practical application of GANs and pixel translation, which improves on the techniques of virtual try-on prior to these new discoveries. This survey details the importance of virtual try-on systems and the history of virtual try-on; shows how GANs, pixel translation, and perceptual losses have influenced the field; and summarizes the latest research in creating virtual try-on systems. Additionally, the authors present the future directions of research to improve virtual try-on systems by making them usable, faster, more effective. By walking through the steps of virtual try-on from start to finish, the chapter aims to expose readers to key concepts shared by many GAN applications and to give readers a solid foundation to pursue further topics in GANs
WORK TOGETHER… WHEN APART CHALLENGES AND WHAT IS NEED FOR EFFECTIVE VIRTUAL TEAMS
Increasingly competitive global markets and accelerating technological changes have increased the need for people to contact via electronic medium to have daily updates, the people those who could not able to meet face to face every day. Those who contact via electronic medium i.e. Virtual Team, are having number of benefit but to achieve these potential benefits, however, leaders need to overcome liabilities inherent in the lack of direct contact among team members and managers. Team members may not naturally know how to interact effectively across space and time. By this paper author try to throw some lights on the challenges that virtual team faces and try to elaborate what is needed for Virtual Team
Dual-Branch Collaborative Transformer for Virtual Try-On
Image-based virtual try-on has recently gained a lot of attention in both the scientific and fashion industry communities due to its challenging setting and practical real-world applications. While pure convolutional approaches have been explored to solve the task, Transformer-based architectures have not received significant attention yet. Following the intuition that self- and cross-attention operators can deal with long-range dependencies and hence improve the generation, in this paper we extend a Transformer-based virtual try-on model by adding a dual-branch collaborative module that can exploit cross-modal information at generation time. We perform experiments on the VITON dataset, which is the standard benchmark for the task, and on a recently collected virtual try-on dataset with multi-category clothing, Dress Code. Experimental results demonstrate the effectiveness of our solution over previous methods and show that Transformer-based architectures can be a viable alternative for virtual try-on
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