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Found 21 papers, 8 papers with code
An Error-Matching Exclusion Method for Accelerating Visual SLAM
In Visual SLAM, achieving accurate feature matching consumes a significant amount of time, severely impacting the real-time performance of the system.
A Feature Matching Method Based on Multi-Level Refinement Strategy
Feature matching is a fundamental and crucial process in visual SLAM, and precision has always been a challenging issue in feature matching.
A Robust Error-Resistant View Selection Method for 3D Reconstruction
To address the issue of increased triangulation uncertainty caused by selecting views with small camera baselines in Structure from Motion (SFM) view selection, this paper proposes a robust error-resistant view selection method.
Region Feature Descriptor Adapted to High Affine Transformations
To address the issue of feature descriptors being ineffective in representing grayscale feature information when images undergo high affine transformations, leading to a rapid decline in feature matching accuracy, this paper proposes a region feature descriptor based on simulating affine transformations using classification.
A Highlight Removal Method for Capsule Endoscopy Images
The images captured by Wireless Capsule Endoscopy (WCE) always exhibit specular reflections, and removing highlights while preserving the color and texture in the region remains a challenge.
Learning Disentangled Avatars with Hybrid 3D Representations
Towards this end, both explicit and implicit 3D representations are heavily studied for a holistic modeling and capture of the whole human (e. g., body, clothing, face and hair), but neither representation is an optimal choice in terms of representation efficacy since different parts of the human avatar have different modeling desiderata.
SCULPT: Shape-Conditioned Unpaired Learning of Pose-dependent Clothed and Textured Human Meshes
We present SCULPT, a novel 3D generative model for clothed and textured 3D meshes of humans.
BEDLAM: A Synthetic Dataset of Bodies Exhibiting Detailed Lifelike Animated Motion
BEDLAM is useful for a variety of tasks and all images, ground truth bodies, 3D clothing, support code, and more are available for research purposes.
AG3D: Learning to Generate 3D Avatars from 2D Image Collections
The key to progress is hence to learn generative models of 3D avatars from abundant unstructured 2D image collections.
ECON: Explicit Clothed humans Optimized via Normal integration
To increase robustness for these cases, existing work uses an explicit parametric body model to constrain surface reconstruction, but this limits the recovery of free-form surfaces such as loose clothing that deviates from the body.
Capturing and Animation of Body and Clothing from Monocular Video
Building on this insight, we propose SCARF (Segmented Clothed Avatar Radiance Field), a hybrid model combining a mesh-based body with a neural radiance field.
Shale oil production prediction and fracturing optimization based on machine learning
The results reveal that DNN exhibit best production prediction accuracy compared to RF and SVM.
Neural Point-based Shape Modeling of Humans in Challenging Clothing
Specifically, we extend point-based methods with a coarse stage, that replaces canonicalization with a learned pose-independent "coarse shape" that can capture the rough surface geometry of clothing like skirts.
gDNA: Towards Generative Detailed Neural Avatars
Furthermore, we show that our method can be used on the task of fitting human models to raw scans, outperforming the previous state-of-the-art.
ICON: Implicit Clothed humans Obtained from Normals
First, ICON infers detailed clothed-human normals (front/back) conditioned on the SMPL(-X) normals.
Ranked #1 on
3D Human Reconstruction
on CAPE
The Power of Points for Modeling Humans in Clothing
The geometry feature can be optimized to fit a previously unseen scan of a person in clothing, enabling the scan to be reposed realistically.
SCALE: Modeling Clothed Humans with a Surface Codec of Articulated Local Elements
We demonstrate the efficacy of our surface representation by learning models of complex clothing from point clouds.
SCANimate: Weakly Supervised Learning of Skinned Clothed Avatar Networks
We present SCANimate, an end-to-end trainable framework that takes raw 3D scans of a clothed human and turns them into an animatable avatar.
Grasping Field: Learning Implicit Representations for Human Grasps
Specifically, our generative model is able to synthesize high-quality human grasps, given only on a 3D object point cloud.
Learning to Dress 3D People in Generative Clothing
To our knowledge, this is the first generative model that directly dresses 3D human body meshes and generalizes to different poses.
Analyzing Clothing Layer Deformation Statistics of 3D Human Motions
Recent capture technologies and methods allow not only to retrieve 3D model sequence of moving people in clothing, but also to separate and extract the underlying body geometry, motion component and the clothing as a geometric layer.
