Search Results for author:
Found 26 papers, 6 papers with code
Text2HOI: Text-guided 3D Motion Generation for Hand-Object Interaction
For contact generation, a VAE-based network takes as input a text and an object mesh, and generates the probability of contacts between the surfaces of hands and the object during the interaction.
Benchmarks and Challenges in Pose Estimation for Egocentric Hand Interactions with Objects
We interact with the world with our hands and see it through our own (egocentric) perspective.
Exploiting Style Latent Flows for Generalizing Deepfake Detection Video Detection
This paper presents a new approach for the detection of fake videos, based on the analysis of style latent vectors and their abnormal behavior in temporal changes in the generated videos.
VLM-PL: Advanced Pseudo Labeling approach Class Incremental Object Detection with Vision-Language Model
This technique uses Vision-Language Model (VLM) to verify the correctness of pseudo ground-truths (GTs) without requiring additional model training.
SDDGR: Stable Diffusion-based Deep Generative Replay for Class Incremental Object Detection
In the field of class incremental learning (CIL), genera- tive replay has become increasingly prominent as a method to mitigate the catastrophic forgetting, alongside the con- tinuous improvements in generative models.
Class Incremental Learning
Class-Incremental Object Detection
+3
3D Reconstruction of Interacting Multi-Person in Clothing from a Single Image
This paper introduces a novel pipeline to reconstruct the geometry of interacting multi-person in clothing on a globally coherent scene space from a single image.
Dynamic Appearance Modeling of Clothed 3D Human Avatars using a Single Camera
For implicit modeling, an implicit network combines the appearance and 3D motion features to decode high-fidelity clothed 3D human avatars with motion-dependent geometry and texture.
Class-Wise Buffer Management for Incremental Object Detection: An Effective Buffer Training Strategy
In this paper, we introduce an effective buffer training strategy (eBTS) that creates the optimized replay buffer on object detection.
BoIR: Box-Supervised Instance Representation for Multi-Person Pose Estimation
Our new instance embedding loss provides a learning signal on the entire area of the image with bounding box annotations, achieving globally consistent and disentangled instance representation.
IFaceUV: Intuitive Motion Facial Image Generation by Identity Preservation via UV map
We proposed IFaceUV, a fully differentiable pipeline that properly combines 2D and 3D information to conduct the facial reenactment task.
Transformer-Based Unified Recognition of Two Hands Manipulating Objects
In our framework, we insert the whole image depicting two hands, an object and their interactions as input and jointly estimate 3 information from each frame: poses of two hands, pose of an object and object types.
HOReeNet: 3D-aware Hand-Object Grasping Reenactment
At the same time, to obtain high-quality 2D images from 3D space, well-designed 3D-to-2D projection and image refinement are required.
Image-free Domain Generalization via CLIP for 3D Hand Pose Estimation
The manipulated image features are then exploited to train the hand pose estimation network via the contrastive learning framework.
Multi-Person 3D Pose and Shape Estimation via Inverse Kinematics and Refinement
To tackle the challenges, we propose a coarse-to-fine pipeline that benefits from 1) inverse kinematics from the occlusion-robust 3D skeleton estimation and 2) Transformer-based relation-aware refinement techniques.
Ranked #1 on
3D Multi-Person Pose Estimation
on MuPoTS-3D
(using extra training data)
Transformer-based Global 3D Hand Pose Estimation in Two Hands Manipulating Objects Scenarios
In this challenge, we aim to estimate global 3D hand poses from the input image where two hands and an object are interacting on the egocentric viewpoint.
Transformer-based Action recognition in hand-object interacting scenarios
This report describes the 2nd place solution to the ECCV 2022 Human Body, Hands, and Activities (HBHA) from Egocentric and Multi-view Cameras Challenge: Action Recognition.
End-to-End Detection and Pose Estimation of Two Interacting Hands
Three dimensional hand pose estimation has reached a level of maturity, enabling real-world applications for single-hand cases.
Weakly-Supervised Domain Adaptation via GAN and Mesh Model for Estimating 3D Hand Poses Interacting Objects
Via the domain adaption in image space, not only 3D HPE accuracy is improved, but also HOI input images are translated to segmented and de-occluded hand-only images.
Measuring Generalisation to Unseen Viewpoints, Articulations, Shapes and Objects for 3D Hand Pose Estimation under Hand-Object Interaction
To address these issues, we designed a public challenge (HANDS'19) to evaluate the abilities of current 3D hand pose estimators (HPEs) to interpolate and extrapolate the poses of a training set.
Sampling Strategies for GAN Synthetic Data
Unlike previous studies of randomly augmenting the synthetic data with real data, we present our simple, effective and easy to implement synthetic data sampling methods to train deep CNNs more efficiently and accurately.
Pushing the Envelope for RGB-based Dense 3D Hand Pose Estimation via Neural Rendering
Once the model is successfully fitted to input RGB images, its meshes i. e. shapes and articulations, are realistic, and we augment view-points on top of estimated dense hand poses.
Augmented Skeleton Space Transfer for Depth-based Hand Pose Estimation
By training the HPG and HPE in a single unified optimization framework enforcing that 1) the HPE agrees with the paired depth and skeleton entries; and 2) the HPG-HPE combination satisfies the cyclic consistency (both the input and the output of HPG-HPE are skeletons) observed via the newly generated unpaired skeletons, our algorithm constructs a HPE which is robust to variations that go beyond the coverage of the existing database.
Deep Convolutional Decision Jungle for Image Classification
Each response map-or node-in both the convolutional and fully-connected layers selectively respond to class labels s. t.
Ranked #168 on
Image Classification
on CIFAR-100
(using extra training data)
First-Person Hand Action Benchmark with RGB-D Videos and 3D Hand Pose Annotations
Our dataset and experiments can be of interest to communities of 3D hand pose estimation, 6D object pose, and robotics as well as action recognition.
Real-time Online Action Detection Forests using Spatio-temporal Contexts
Online action detection (OAD) is challenging since 1) robust yet computationally expensive features cannot be straightforwardly used due to the real-time processing requirements and 2) the localization and classification of actions have to be performed even before they are fully observed.
Kinematic-Layout-aware Random Forests for Depth-based Action Recognition
In this paper, we tackle the problem of 24 hours-monitoring patient actions in a ward such as "stretching an arm out of the bed", "falling out of the bed", where temporal movements are subtle or significant.
