170 citations · 476 across the 13 of their papers we have counts for
14 papers
AssemblyHands: Towards Egocentric Activity Understanding via 3D Hand Pose Estimation
Takehiko Ohkawa, Kun He, Fadime Sener +3
We present AssemblyHands, a large-scale benchmark dataset with accurate 3D hand pose annotations, to facilitate the study of egocentric activities with challenging hand-object inte…
In-Hand 3D Object Scanning from an RGB Sequence
Shreyas Hampali, Tomas Hodan, Luan Tran +3
We propose a method for in-hand 3D scanning of an unknown object with a monocular camera. Our method relies on a neural implicit surface representation that captures both the geome…
UmeTrack: Unified multi-view end-to-end hand tracking for VR
Shangchen Han, Po-chen Wu, Yubo Zhang +13
Real-time tracking of 3D hand pose in world space is a challenging problem and plays an important role in VR interaction. Existing work in this space are limited to either producin…
Neural Correspondence Field for Object Pose Estimation
Lin Huang, Tomas Hodan, Lingni Ma +7
We propose a method for estimating the 6DoF pose of a rigid object with an available 3D model from a single RGB image. Unlike classical correspondence-based methods which predict 3…
Fully Understanding Generic Objects: Modeling, Segmentation, and Reconstruction
Feng Liu, Luan Tran, Xiaoming Liu
Inferring 3D structure of a generic object from a 2D image is a long-standing objective of computer vision. Conventional approaches either learn completely from CAD-generated synth…
On Learning Disentangled Representations for Gait Recognition
Ziyuan Zhang, Luan Tran, Feng Liu +1
Gait, the walking pattern of individuals, is one of the important biometrics modalities. Most of the existing gait recognition methods take silhouettes or articulated body models a…
Towards High-fidelity Nonlinear 3D Face Morphable Model
Luan Tran, Feng Liu, Xiaoming Liu
Embedding 3D morphable basis functions into deep neural networks opens great potential for models with better representation power. However, to faithfully learn those models from a…
Gait Recognition via Disentangled Representation Learning
Ziyuan Zhang, Luan Tran, Xi Yin +4
Gait, the walking pattern of individuals, is one of the most important biometrics modalities. Most of the existing gait recognition methods take silhouettes or articulated body mod…
3D Face Modeling From Diverse Raw Scan Data
Feng Liu, Luan Tran, Xiaoming Liu
Traditional 3D face models learn a latent representation of faces using linear subspaces from limited scans of a single database. The main roadblock of building a large-scale face…
On Learning 3D Face Morphable Model from In-the-wild Images
Luan Tran, Xiaoming Liu
As a classic statistical model of 3D facial shape and albedo, 3D Morphable Model (3DMM) is widely used in facial analysis, e.g., model fitting, image synthesis. Conventional 3DMM i…
Towards Interpretable Face Recognition
Bangjie Yin, Luan Tran, Haoxiang Li +2
Deep CNNs have been pushing the frontier of visual recognition over past years. Besides recognition accuracy, strong demands in understanding deep CNNs in the research community mo…
Nonlinear 3D Face Morphable Model
Luan Tran, Xiaoming Liu
As a classic statistical model of 3D facial shape and texture, 3D Morphable Model (3DMM) is widely used in facial analysis, e.g., model fitting, image synthesis. Conventional 3DMM…
Gotta Adapt 'Em All: Joint Pixel and Feature-Level Domain Adaptation for Recognition in the Wild
Luan Tran, Kihyuk Sohn, Xiang Yu +2
Recent developments in deep domain adaptation have allowed knowledge transfer from a labeled source domain to an unlabeled target domain at the level of intermediate features or in…
Representation Learning by Rotating Your Faces
Luan Tran, Xi Yin, Xiaoming Liu
The large pose discrepancy between two face images is one of the fundamental challenges in automatic face recognition. Conventional approaches to pose-invariant face recognition ei…