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Found 15 papers, 2 papers with code
SceneSketcher: Fine-Grained Image Retrieval with Scene Sketches
Sketch-based image retrieval (SBIR) has been a popular research topic in recent years.
HOGSA: Bimanual Hand-Object Interaction Understanding with 3D Gaussian Splatting Based Data Augmentation
Understanding of bimanual hand-object interaction plays an important role in robotics and virtual reality.
Universal Features Guided Zero-Shot Category-Level Object Pose Estimation
Object pose estimation, crucial in computer vision and robotics applications, faces challenges with the diversity of unseen categories.
Diffgrasp: Whole-Body Grasping Synthesis Guided by Object Motion Using a Diffusion Model
Generating high-quality whole-body human object interaction motion sequences is becoming increasingly important in various fields such as animation, VR/AR, and robotics.
Multi-GraspLLM: A Multimodal LLM for Multi-Hand Semantic Guided Grasp Generation
Multi-hand semantic grasp generation aims to generate feasible and semantically appropriate grasp poses for different robotic hands based on natural language instructions.
Complementing Event Streams and RGB Frames for Hand Mesh Reconstruction
Experiments on real-world data demonstrate that EvRGBHand can effectively solve the challenging issues when using either type of camera alone via retaining the merits of both, and shows the potential of generalization to outdoor scenes and another type of event camera.
Self-supervised Learning of Implicit Shape Representation with Dense Correspondence for Deformable Objects
In this paper, we propose a novel self-supervised approach to learn neural implicit shape representation for deformable objects, which can represent shapes with a template shape and dense correspondence in 3D.
Novel-view Synthesis and Pose Estimation for Hand-Object Interaction from Sparse Views
Hand-object interaction understanding and the barely addressed novel view synthesis are highly desired in the immersive communication, whereas it is challenging due to the high deformation of hand and heavy occlusions between hand and object.
EvHandPose: Event-based 3D Hand Pose Estimation with Sparse Supervision
Experiments on EvRealHands demonstrate that EvHandPose outperforms previous event-based methods under all evaluation scenes, achieves accurate and stable hand pose estimation with high temporal resolution in fast motion and strong light scenes compared with RGB-based methods, generalizes well to outdoor scenes and another type of event camera, and shows the potential for the hand gesture recognition task.
Efficient Virtual View Selection for 3D Hand Pose Estimation
3D hand pose estimation from single depth is a fundamental problem in computer vision, and has wide applications. However, the existing methods still can not achieve satisfactory hand pose estimation results due to view variation and occlusion of human hand.
Ranked #1 on
Hand Pose Estimation
on ICVL Hands
E-ffective: A Visual Analytic System for Exploring the Emotion and Effectiveness of Inspirational Speeches
We further studied the usability of the system by speaking novices and experts on assisting analysis of inspirational speech effectiveness.
Sequential 3D Human Pose Estimation Using Adaptive Point Cloud Sampling Strategy
In this paper, we present a new perspective on the 3D human pose estimation method from point cloud sequences.
Ranked #1 on
Pose Estimation
on ITOP front-view
(using extra training data)
Interacting Two-Hand 3D Pose and Shape Reconstruction From Single Color Image
In this paper, we propose a novel deep learning framework to reconstruct 3D hand poses and shapes of two interacting hands from a single color image.
Ranked #7 on
3D Interacting Hand Pose Estimation
on InterHand2.6M
Hand3D: Hand Pose Estimation using 3D Neural Network
We propose a novel 3D neural network architecture for 3D hand pose estimation from a single depth image.
Joint Hand Detection and Rotation Estimation by Using CNN
Hand detection is essential for many hand related tasks, e. g. parsing hand pose, understanding gesture, which are extremely useful for robotics and human-computer interaction.
