Search Results for author:
Found 18 papers, 14 papers with code
LOGen: Toward Lidar Object Generation by Point Diffusion
A common strategy to improve lidar segmentation results on rare semantic classes consists of pasting objects from one lidar scene into another.
MILAN: Milli-Annotations for Lidar Semantic Segmentation
In a second step, we leverage the same self-supervised representations to cluster points in our selected scans.
PAFUSE: Part-based Diffusion for 3D Whole-Body Pose Estimation
We introduce a novel approach for 3D whole-body pose estimation, addressing the challenge of scale -- and deformability -- variance across body parts brought by the challenge of extending the 17 major joints on the human body to fine-grained keypoints on the face and hands.
Valeo4Cast: A Modular Approach to End-to-End Forecasting
In end-to-end forecasting, the model must jointly detect and track from sensor data (cameras or LiDARs) the past trajectories of the different elements of the scene and predict their future locations.
ManiPose: Manifold-Constrained Multi-Hypothesis 3D Human Pose Estimation
By constraining the outputs to lie on the human pose manifold, ManiPose guarantees the consistency of all hypothetical poses, in contrast to previous works.
WAIT: Feature Warping for Animation to Illustration video Translation using GANs
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.
You Never Get a Second Chance To Make a Good First Impression: Seeding Active Learning for 3D Semantic Segmentation
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.
Improving Sketch Colorization using Adversarial Segmentation Consistency
We leverage semantic image segmentation from a general-purpose panoptic segmentation network to generate an additional adversarial loss function.
H3WB: Human3.6M 3D WholeBody Dataset and Benchmark
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 Facial Landmark Localization
on H3WB
A Simple and Powerful Global Optimization for Unsupervised Video Object Segmentation
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)
WiCV 2022: The Tenth Women In Computer Vision Workshop
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.
HPRNet: Hierarchical Point Regression for Whole-Body Human Pose Estimation
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
HoughNet: Integrating near and long-range evidence for visual detection
This paper presents HoughNet, a one-stage, anchor-free, voting-based, bottom-up object detection method.
Adversarial Segmentation Loss for Sketch Colorization
Our loss function can be integrated to any baseline GAN model.
Reducing Label Noise in Anchor-Free Object Detection
In this paper, we propose a new labeling strategy aimed to reduce the label noise in anchor-free detectors.
Ranked #125 on
Object Detection
on COCO test-dev
HoughNet: Integrating near and long-range evidence for bottom-up object detection
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 #107 on
Object Detection
on COCO minival
GANILLA: Generative Adversarial Networks for Image to Illustration Translation
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.
DRAW: Deep networks for Recognizing styles of Artists Who illustrate children's books
The style was noticeable in other characters of the same illustrator in different books as well.
