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Found 13 papers, 4 papers with code
FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation
We present FoundHand, a large-scale domain-specific diffusion model for synthesizing single and dual hand images.
HOT3D: Hand and Object Tracking in 3D from Egocentric Multi-View Videos
We introduce HOT3D, a publicly available dataset for egocentric hand and object tracking in 3D.
EgoPoseFormer: A Simple Baseline for Stereo Egocentric 3D Human Pose Estimation
We also show that our method can be seamlessly extended to monocular settings, which achieves state-of-the-art performance on the SceneEgo dataset, improving MPJPE by 25. 5mm (21% improvement) compared to the best existing method with only 60. 7% model parameters and 36. 4% FLOPs.
Ranked #1 on
Egocentric Pose Estimation
on UnrealEgo
Social Diffusion: Long-term Multiple Human Motion Anticipation
We propose Social Diffusion, a novel method for short-term and long-term forecasting of the motion of multiple persons as well as their social interactions.
UmeTrack: Unified multi-view end-to-end hand tracking for VR
In this paper, we present a unified end-to-end differentiable framework for multi-view, multi-frame hand tracking that directly predicts 3D hand pose in world space.
Identity-Aware Hand Mesh Estimation and Personalization from RGB Images
In this paper, we propose an identity-aware hand mesh estimation model, which can incorporate the identity information represented by the intrinsic shape parameters of the subject.
Neural Correspondence Field for Object Pose Estimation
Unlike classical correspondence-based methods which predict 3D object coordinates at pixels of the input image, the proposed method predicts 3D object coordinates at 3D query points sampled in the camera frustum.
Learning Local Descriptors With a CDF-Based Dynamic Soft Margin
The triplet loss is adopted by a variety of learning tasks, such as local feature descriptor learning.
Accelerating Large-Kernel Convolution Using Summed-Area Tables
In this work, we explore using learnable box filters to allow for convolution with arbitrarily large kernel size, while keeping the number of parameters per filter constant.
Learning to Detect Features in Texture Images
Local feature detection is a fundamental task in computer vision, and hand-crafted feature detectors such as SIFT have shown success in applications including image-based localization and registration.
High-Precision Localization Using Ground Texture
We introduce an image-based global localization system that is accurate to a few millimeters and performs reliable localization both indoors and outside.
Robot In a Room: Toward Perfect Object Recognition in Closed Environments
By constraining a robot to stay in a limited territory, we can ensure that the robot has seen most objects before and the speed of introducing a new object is slow.
3D ShapeNets: A Deep Representation for Volumetric Shapes
Our model, 3D ShapeNets, learns the distribution of complex 3D shapes across different object categories and arbitrary poses from raw CAD data, and discovers hierarchical compositional part representations automatically.
Ranked #34 on
3D Point Cloud Classification
on ModelNet40
(Mean Accuracy metric)
