Single Stage Virtual Try-on via Deformable Attention Flows

Virtual try-on aims to generate a photo-realistic fitting result given an in-shop garment and a reference person image. Existing methods usually build up multi-stage frameworks to deal with clothes warping and body blending respectively, or rely heavily on intermediate parser-based labels which may be noisy or even inaccurate. To solve the above challenges, we propose a single-stage try-on framework by developing a novel Deformable Attention Flow (DAFlow), which applies the deformable attention scheme to multi-flow estimation. With pose keypoints as the guidance only, the self- and cross-deformable attention flows are estimated for the reference person and the garment images, respectively. By sampling multiple flow fields, the feature-level and pixel-level information from different semantic areas are simultaneously extracted and merged through the attention mechanism. It enables clothes warping and body synthesizing at the same time which leads to photo-realistic results in an end-to-end manner. Extensive experiments on two try-on datasets demonstrate that our proposed method achieves state-of-the-art performance both qualitatively and quantitatively. Furthermore, additional experiments on the other two image editing tasks illustrate the versatility of our method for multi-view synthesis and image animation.Comment: ECCV 202

Percutaneous Nephrolithotomy under Local Infiltration Anesthesia in Kneeling Prone Position for a Patient with Spinal Deformity

Urolithiasis, a common condition in patients with spinal deformity, poses a challenge to surgical procedures and anesthetic management. A 51-year-old Chinese male presented with bilateral complex renal calculi. He was also affected by severe kyphosis deformity and spinal stiffness due to ankylosing spondylitis. Dr. Li performed the percutaneous nephrolithotomy under local infiltration anesthesia with the patient in a kneeling prone position, achieving satisfactory stone clearance with no severe complications. We found this protocol safe and effective to manage kidney stones in patients with spinal deformity. Local infiltration anesthesia may benefit patients for whom epidural anesthesia and intubation anesthesia are difficult

SOOD: Towards Semi-Supervised Oriented Object Detection

Semi-Supervised Object Detection (SSOD), aiming to explore unlabeled data for boosting object detectors, has become an active task in recent years. However, existing SSOD approaches mainly focus on horizontal objects, leaving multi-oriented objects that are common in aerial images unexplored. This paper proposes a novel Semi-supervised Oriented Object Detection model, termed SOOD, built upon the mainstream pseudo-labeling framework. Towards oriented objects in aerial scenes, we design two loss functions to provide better supervision. Focusing on the orientations of objects, the first loss regularizes the consistency between each pseudo-label-prediction pair (includes a prediction and its corresponding pseudo label) with adaptive weights based on their orientation gap. Focusing on the layout of an image, the second loss regularizes the similarity and explicitly builds the many-to-many relation between the sets of pseudo-labels and predictions. Such a global consistency constraint can further boost semi-supervised learning. Our experiments show that when trained with the two proposed losses, SOOD surpasses the state-of-the-art SSOD methods under various settings on the DOTA-v1.5 benchmark. The code will be available at https://github.com/HamPerdredes/SOOD.Comment: Accepted to CVPR 2023. Code will be available at https://github.com/HamPerdredes/SOO