12 accepted papers
Current virtual try-on methods primarily enhance performance through network optimization, employing strategies such as coarse-to-fine structures and ReferenceNet to inject clothing information. However, these methods are often constrained by the limited quantity and diversity of training samples, w…
A good garment try-on model should learn the transfer between different types of garments while satisfying: 1) high fidelity and 2) low inference speed. Existing methods address either of these two issues, limited processing speed or low generation quality. We directly use a lightweight encoder-deco…
Efficiently applying fully supervised learning to virtual try-on tasks is challenging due to the lack of paired ground truth in available training samples. Recent works have achieved virtual try-ons by employing self-supervised learning-based inpainting paradigms. However, this approach is heavily d…
A good virtual try-on model should introduce minimal redundant conditional information to avoid instability and increase inference efficiency. Existing methods rely on inpainting masks to guide the generation of the object, but the masks, generated by unstable human parsers, often produce unreliable
This paper investigates the occlusion problems in virtual try-on (VTON) tasks. According to how they affect the try-on results, the occlusion issues of existing VTON methods can be grouped into two categories: (1) Inherent Occlusions, which are the ghosts of the clothing from reference input images…
Combining omnimodal feature interaction using LiDAR, surround-view camera, and Radar to form a network has a great guarantee for the safety of autonomous driving, but most of the current omnimodal fusion methods focus on the interaction enhancement of LiDAR and surround-view camera, ignoring the foc…
Image-based virtual try-on aims to transfer a target clothing onto a specific person. A significant challenge is arbitrarily matched clothing and person lack corresponding ground truth to supervised learning. A recent pioneering work leveraged an improved cycleGAN to enable one network to generate t…
LiDAR is a key sensor for accurately sensing of the environment in autonomous driving. While existing 3D object detection methods generally rely on data augmentation and feature fusion to improve performance, the challenge of dealing with sample imbalance is often overlooked. We design a novel 3D de…
The multi-pose virtual try-on technology aims to seamlessly fit an in-shop garment onto a reference person in various poses. This technology has attracted considerable attention from researchers due to its potential commercial and practical applications. Previous works in this field have encountered…
Image-based virtual try-on tasks remain challenging, primarily due to inherent complexities associated with non-rigid garment deformation modeling and strong feature entanglement of clothing within human body. Recent groundbreaking formulations, such as in-painting, cycle consistency, and knowledge…
With the growing trend of virtual try-on, multi-pose tasks attract researchers due to their higher commercial value. Prior methods lack an effective geometric deformation to maintain the original image details resulting in many details loss in the head and garment. To address this problem, we propos…
3D virtual try-on receives widespread attention from scholars due to its great practical and commercial values. In prior methods, the fundamental problems lie in the limitations on texture retention during garment deformation and the lack of feature context capture during depth estimation. To addres…