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Found 16 papers, 6 papers with code
AssemblyHands: Towards Egocentric Activity Understanding via 3D Hand Pose Estimation
To obtain high-quality 3D hand pose annotations for the egocentric images, we develop an efficient pipeline, where we use an initial set of manual annotations to train a model to automatically annotate a much larger dataset.
In-Hand 3D Object Scanning from an RGB Sequence
As direct optimization over all shape and pose parameters is prone to fail without coarse-level initialization, we propose an incremental approach that starts by splitting the sequence into carefully selected overlapping segments within which the optimization is likely to succeed.
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.
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.
Fully Understanding Generic Objects: Modeling, Segmentation, and Reconstruction
That is, for a 2D image of a generic object, we decompose it into latent representations of category, shape and albedo, lighting and camera projection matrix, decode the representations to segmented 3D shape and albedo respectively, and fuse these components to render an image well approximating the input image.
On Learning Disentangled Representations for Gait Recognition
The LSTM integrates pose features over time as a dynamic gait feature while canonical features are averaged as a static gait feature.
Gait Recognition via Disentangled Representation Learning
Most of the existing gait recognition methods take silhouettes or articulated body models as the gait features.
Towards High-fidelity Nonlinear 3D Face Morphable Model
By improving the nonlinear 3D morphable model in both learning objective and network architecture, we present a model which is superior in capturing higher level of details than the linear or its precedent nonlinear counterparts.
Ranked #23 on
3D Face Reconstruction
on REALY
3D Face Modeling From Diverse Raw Scan Data
Traditional 3D face models learn a latent representation of faces using linear subspaces from limited scans of a single database.
On Learning 3D Face Morphable Model from In-the-wild Images
To address these problems, this paper proposes an innovative framework to learn a nonlinear 3DMM model from a large set of in-the-wild face images, without collecting 3D face scans.
Towards Interpretable Face Recognition
Deep CNNs have been pushing the frontier of visual recognition over past years.
Nonlinear 3D Face Morphable Model
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.
Ranked #2 on
Face Alignment
on AFLW2000
Gotta Adapt 'Em All: Joint Pixel and Feature-Level Domain Adaptation for Recognition in the Wild
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 input pixels.
Missing Modalities Imputation via Cascaded Residual Autoencoder
To leverage the valuable information in the corrupted data, we propose to impute the missing data by leveraging the relatedness among different modalities.
Disentangled Representation Learning GAN for Pose-Invariant Face Recognition
The large pose discrepancy between two face images is one of the key challenges in face recognition.
Representation Learning by Rotating Your Faces
First, the encoder-decoder structure of the generator enables DR-GAN to learn a representation that is both generative and discriminative, which can be used for face image synthesis and pose-invariant face recognition.
