Monocular 3D human pose estimation (3D-HPE) is an inherently ambiguous task, as a 2D pose in an image might originate from different possible 3D poses.
Current state-of-the-art video-to-video translation models rely on having a video sequence or a single style image to stylize an input video.
Assuming that images of the point clouds are available, which is common, our method relies on powerful unsupervised image features to measure the diversity of the point clouds.
We leverage semantic image segmentation from a general-purpose panoptic segmentation network to generate an additional adversarial loss function.
We also propose three tasks: i) 3D whole-body pose lifting from 2D complete whole-body pose, ii) 3D whole-body pose lifting from 2D incomplete whole-body pose, and iii) 3D whole-body pose estimation from a single RGB image.
Ranked #2 on 3D Human Pose Estimation on H3WB
We propose a simple, yet powerful approach for unsupervised object segmentation in videos.
Ranked #1 on Unsupervised Video Object Segmentation on SegTrack v2 (Jaccard (Mean) metric)
In this paper, we present the details of Women in Computer Vision Workshop - WiCV 2022, organized alongside the hybrid CVPR 2022 in New Orleans, Louisiana.
Differently, in whole-body pose estimation, the locations of fine-grained keypoints (68 on face, 21 on each hand and 3 on each foot) are estimated as well, which creates a scale variance problem that needs to be addressed.
Ranked #1 on Facial Landmark Detection on COCO-WholeBody
This paper presents HoughNet, a one-stage, anchor-free, voting-based, bottom-up object detection method.
In this paper, we propose a new labeling strategy aimed to reduce the label noise in anchor-free detectors.
Ranked #123 on Object Detection on COCO test-dev
We further validate the effectiveness of our proposal in another task, namely, "labels to photo" image generation by integrating the voting module of HoughNet to two different GAN models and showing that the accuracy is significantly improved in both cases.
Ranked #100 on Object Detection on COCO minival
To address this problem, we propose a new framework for the quantitative evaluation of image-to-illustration models, where both content and style are taken into account using separate classifiers.
The style was noticeable in other characters of the same illustrator in different books as well.