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Found 42 papers, 16 papers with code
Magic-Boost: Boost 3D Generation with Mutli-View Conditioned Diffusion
Benefiting from the rapid development of 2D diffusion models, 3D content creation has made significant progress recently.
An edge detection-based deep learning approach for tear meniscus height measurement
For improved segmentation of the pupil and tear meniscus areas, the convolutional neural network Inceptionv3 was first implemented as an image quality assessment model, effectively identifying higher-quality images with an accuracy of 98. 224%.
Towards a Comprehensive, Efficient and Promptable Anatomic Structure Segmentation Model using 3D Whole-body CT Scans
In this work, we propose a comprehensive and scalable 3D SAM model for whole-body CT segmentation, named CT-SAM3D.
Leveraging Gradients for Unsupervised Accuracy Estimation under Distribution Shift
Our key idea is that the model should be adjusted with a higher magnitude of gradients when it does not generalize to the test dataset with a distribution shift.
DREAM-Talk: Diffusion-based Realistic Emotional Audio-driven Method for Single Image Talking Face Generation
The generation of emotional talking faces from a single portrait image remains a significant challenge.
AvatarStudio: High-fidelity and Animatable 3D Avatar Creation from Text
We study the problem of creating high-fidelity and animatable 3D avatars from only textual descriptions.
ViT-Lens: Towards Omni-modal Representations
In this paper, we present ViT-Lens-2 that facilitates efficient omni-modal representation learning by perceiving novel modalities with a pretrained ViT and aligning them to a pre-defined space.
MagicAnimate: Temporally Consistent Human Image Animation using Diffusion Model
Existing animation works typically employ the frame-warping technique to animate the reference image towards the target motion.
Continual Learning via Manifold Expansion Replay
To address this problem, we propose a novel replay strategy called Manifold Expansion Replay (MaER).
Automatic nodule identification and differentiation in ultrasound videos to facilitate per-nodule examination
Ultrasound is a vital diagnostic technique in health screening, with the advantages of non-invasive, cost-effective, and radiation free, and therefore is widely applied in the diagnosis of nodules.
GETAvatar: Generative Textured Meshes for Animatable Human Avatars
We study the problem of 3D-aware full-body human generation, aiming at creating animatable human avatars with high-quality textures and geometries.
MagicAvatar: Multimodal Avatar Generation and Animation
This report presents MagicAvatar, a framework for multimodal video generation and animation of human avatars.
MagicEdit: High-Fidelity and Temporally Coherent Video Editing
In this report, we present MagicEdit, a surprisingly simple yet effective solution to the text-guided video editing task.
A Co-training Approach for Noisy Time Series Learning
Based on this, we create two views for the input time series through two different encoders.
ViT-Lens: Initiating Omni-Modal Exploration through 3D Insights
A well-trained lens with a ViT backbone has the potential to serve as one of these foundation models, supervising the learning of subsequent modalities.
Ranked #2 on
Zero-Shot Transfer 3D Point Cloud Classification
on ModelNet40
(using extra training data)
Parse and Recall: Towards Accurate Lung Nodule Malignancy Prediction like Radiologists
In this paper, we propose a radiologist-inspired method to simulate the diagnostic process of radiologists, which is composed of context parsing and prototype recalling modules.
Contrastive Shapelet Learning for Unsupervised Multivariate Time Series Representation Learning
Recent studies have shown great promise in unsupervised representation learning (URL) for multivariate time series, because URL has the capability in learning generalizable representation for many downstream tasks without using inaccessible labels.
Group Equivariant BEV for 3D Object Detection
Experimental results demonstrate that GeqBevNet can extract more rotational equivariant features in the 3D object detection of the actual road scene and improve the performance of object orientation prediction.
Democratic Policy Decisions with Decentralized Promises Contingent on Vote Outcome
We study pre-vote interactions in a committee that enacts a welfare-improving reform through voting.
OmniAvatar: Geometry-Guided Controllable 3D Head Synthesis
We present OmniAvatar, a novel geometry-guided 3D head synthesis model trained from in-the-wild unstructured images that is capable of synthesizing diverse identity-preserved 3D heads with compelling dynamic details under full disentangled control over camera poses, facial expressions, head shapes, articulated neck and jaw poses.
AgileGAN3D: Few-Shot 3D Portrait Stylization by Augmented Transfer Learning
Capitalizing on the recent advancement of 3D-aware GAN models, we perform \emph{guided transfer learning} on a pretrained 3D GAN generator to produce multi-view-consistent stylized renderings.
Inducing Neural Collapse in Deep Long-tailed Learning
One of the reasons is that the learned representations (i. e. features) from the imbalanced datasets are less effective than those from balanced datasets.
PV3D: A 3D Generative Model for Portrait Video Generation
While some prior works have applied such image GANs to unconditional 2D portrait video generation and static 3D portrait synthesis, there are few works successfully extending GANs for generating 3D-aware portrait videos.
Med-Query: Steerable Parsing of 9-DoF Medical Anatomies with Query Embedding
For rib parsing, CT scans have been annotated at the rib instance-level for quantitative evaluation, similarly for spine vertebrae and abdominal organs.
AvatarGen: A 3D Generative Model for Animatable Human Avatars
Specifically, we decompose the generative 3D human synthesis into pose-guided mapping and canonical representation with predefined human pose and shape, such that the canonical representation can be explicitly driven to different poses and shapes with the guidance of a 3D parametric human model SMPL.
Multi-timescale Event Detection in Nonintrusive Load Monitoring based on MDL Principle
Load event detection is the fundamental step for the event-based non-intrusive load monitoring (NILM).
A New Probabilistic V-Net Model with Hierarchical Spatial Feature Transform for Efficient Abdominal Multi-Organ Segmentation
Accurate and robust abdominal multi-organ segmentation from CT imaging of different modalities is a challenging task due to complex inter- and intra-organ shape and appearance variations among abdominal organs.
AvatarGen: a 3D Generative Model for Animatable Human Avatars
Unsupervised generation of clothed virtual humans with various appearance and animatable poses is important for creating 3D human avatars and other AR/VR applications.
PoseTriplet: Co-evolving 3D Human Pose Estimation, Imitation, and Hallucination under Self-supervision
Existing self-supervised 3D human pose estimation schemes have largely relied on weak supervisions like consistency loss to guide the learning, which, inevitably, leads to inferior results in real-world scenarios with unseen poses.
Ranked #37 on
3D Human Pose Estimation
on MPI-INF-3DHP
Geometry-Guided Progressive NeRF for Generalizable and Efficient Neural Human Rendering
Meanwhile, for achieving higher rendering efficiency, we introduce a progressive rendering pipeline through geometry guidance, which leverages the geometric feature volume and the predicted density values to progressively reduce the number of sampling points and speed up the rendering process.
Direct Multi-view Multi-person 3D Pose Estimation
Instead of estimating 3D joint locations from costly volumetric representation or reconstructing the per-person 3D pose from multiple detected 2D poses as in previous methods, MvP directly regresses the multi-person 3D poses in a clean and efficient way, without relying on intermediate tasks.
Ranked #3 on
3D Multi-Person Pose Estimation
on Panoptic
(using extra training data)
Knothe-Rosenblatt transport for Unsupervised Domain Adaptation
Knothe-Rosenblatt Domain Adaptation (KRDA) is based on the Knothe-Rosenblatt transport: we exploit autoregressive density estimation algorithms to accurately model the different sources by an autoregressive model using a mixture of Gaussians.
PoseAug: A Differentiable Pose Augmentation Framework for 3D Human Pose Estimation
To address this problem, we present PoseAug, a new auto-augmentation framework that learns to augment the available training poses towards a greater diversity and thus improve generalization of the trained 2D-to-3D pose estimator.
Ranked #1 on
Monocular 3D Human Pose Estimation
on Human3.6M
(Use Video Sequence metric)
Body Meshes as Points
In this work, we present a single-stage model, Body Meshes as Points (BMP), to simplify the pipeline and lift both efficiency and performance.
Ranked #9 on
3D Multi-Person Pose Estimation
on MuPoTS-3D
3D Human Shape Estimation
3D Multi-Person Pose Estimation
+1
Mean Field Games Master Equations with Non-separable Hamiltonians and Displacement Monotonicity
In this manuscript, we propose a structural condition on non-separable Hamiltonians, which we term displacement monotonicity condition, to study second order mean field games master equations.
Analysis of PDEs Optimization and Control Probability 35R15, 49N80, 49Q22, 60H30, 91A16, 93E20
EMTL: A Generative Domain Adaptation Approach
We propose an unsupervised domain adaptation approach based on generative models.
Inference Stage Optimization for Cross-scenario 3D Human Pose Estimation
In this work, we propose a novel framework, Inference Stage Optimization (ISO), for improving the generalizability of 3D pose models when source and target data come from different pose distributions.
Ranked #118 on
3D Human Pose Estimation
on 3DPW
(PA-MPJPE metric)
Hierarchical Graph Pooling with Structure Learning
HGP-SL incorporates graph pooling and structure learning into a unified module to generate hierarchical representations of graphs.
Ranked #1 on
Graph Classification
on PROTEINS
Single-Stage Multi-Person Pose Machines
Based on SPR, we develop the SPM model that can directly predict structured poses for multiple persons in a single stage, and thus offer a more compact pipeline and attractive efficiency advantage over two-stage methods.
Ranked #3 on
Keypoint Detection
on MPII Multi-Person
Predicting Path Failure In Time-Evolving Graphs
Through experiments on a real-world telecommunication network and a traffic network in California, we demonstrate the superiority of LRGCN to other competing methods in path failure prediction, and prove the effectiveness of SAPE on path representation.
Interactive Binary Image Segmentation with Edge Preservation
Binary image segmentation plays an important role in computer vision and has been widely used in many applications such as image and video editing, object extraction, and photo composition.
Learning for Disparity Estimation through Feature Constancy
The second part performs matching cost calculation, matching cost aggregation and disparity calculation to estimate the initial disparity using shared features.
