Search Results for author:
Found 54 papers, 15 papers with code
ID-Animator: Zero-Shot Identity-Preserving Human Video Generation
Based on this pipeline, a random face reference training method is further devised to precisely capture the ID-relevant embeddings from reference images, thus improving the fidelity and generalization capacity of our model for ID-specific video generation.
X-Ray: A Sequential 3D Representation for Generation
In this paper, we introduce X-Ray, an innovative approach to 3D generation that employs a new sequential representation, drawing inspiration from the depth-revealing capabilities of X-Ray scans to meticulously capture both the external and internal features of objects.
CRNet: A Detail-Preserving Network for Unified Image Restoration and Enhancement Task
In real-world scenarios, images captured often suffer from blurring, noise, and other forms of image degradation, and due to sensor limitations, people usually can only obtain low dynamic range images.
Bracketing Image Restoration and Enhancement with High-Low Frequency Decomposition
Specifically, we employ two modules for feature extraction: shared weight modules and non-shared weight modules.
FashionEngine: Interactive Generation and Editing of 3D Clothed Humans
FashionEngine automates the 3D human production with three key components: 1) A pre-trained 3D human diffusion model that learns to model 3D humans in a semantic UV latent space from 2D image training data, which provides strong priors for diverse generation and editing tasks.
SurMo: Surface-based 4D Motion Modeling for Dynamic Human Rendering
2) Physical motion decoding that is designed to encourage physical motion learning by decoding the motion triplane features at timestep t to predict both spatial derivatives and temporal derivatives at the next timestep t+1 in the training stage.
StructLDM: Structured Latent Diffusion for 3D Human Generation
2) A structured 3D-aware auto-decoder that factorizes the global latent space into several semantic body parts parameterized by a set of conditional structured local NeRFs anchored to the body template, which embeds the properties learned from the 2D training data and can be decoded to render view-consistent humans under different poses and clothing styles.
Generating Content for HDR Deghosting from Frequency View
To address this challenge, we propose the Low-Frequency aware Diffusion (LF-Diff) model for ghost-free HDR imaging.
Boosting Image Restoration via Priors from Pre-trained Models
Pre-trained models with large-scale training data, such as CLIP and Stable Diffusion, have demonstrated remarkable performance in various high-level computer vision tasks such as image understanding and generation from language descriptions.
Coronary CTA and Quantitative Cardiac CT Perfusion (CCTP) in Coronary Artery Disease
CCTA in conjunction with a new automated quantitative CCTP approach can augment the interpretation of CAD, enabling the distinction of ischemia due to obstructive lesions and MVD.
AI prediction of cardiovascular events using opportunistic epicardial adipose tissue assessments from CT calcium score
Background: Recent studies have used basic epicardial adipose tissue (EAT) assessments (e. g., volume and mean HU) to predict risk of atherosclerosis-related, major adverse cardiovascular events (MACE).
Pericoronary adipose tissue feature analysis in CT calcium score images with comparison to coronary CTA
Preliminarily, PCAT features can be assessed from three main coronary arteries in non-contrast CTCS images with performance characteristics that are at the very least comparable to CCTA.
Enhancing RAW-to-sRGB with Decoupled Style Structure in Fourier Domain
However, current methods often ignore the difference between cell phone RAW images and DSLR camera RGB images, a difference that goes beyond the color matrix and extends to spatial structure due to resolution variations.
Temporal-Spatial Entropy Balancing for Causal Continuous Treatment-Effect Estimation
However, temporal and spatial dimensions are extremely critical in the logistics field, and this limitation may directly affect the precision of subsidy and pricing strategies.
Query by Activity Video in the Wild
In this paper, we propose a visual-semantic embedding network that explicitly deals with the imbalanced scenario for activity retrieval.
Towards High-quality HDR Deghosting with Conditional Diffusion Models
To address this challenge, we formulate the HDR deghosting problem as an image generation that leverages LDR features as the diffusion model's condition, consisting of the feature condition generator and the noise predictor.
Timely Fusion of Surround Radar/Lidar for Object Detection in Autonomous Driving Systems
Surround Radar/Lidar can provide 360-degree view sampling with the minimal cost, which are promising sensing hardware solutions for autonomous driving systems.
Enhancing cardiovascular risk prediction through AI-enabled calcium-omics
We used a Cox model with elastic-net regularization on 2457 CT calcium score (CTCS) enriched for MACE events obtained from a large no-cost CLARIFY program (ClinicalTri-als. gov Identifier: NCT04075162).
HumanLiff: Layer-wise 3D Human Generation with Diffusion Model
In this work, we propose HumanLiff, the first layer-wise 3D human generative model with a unified diffusion process.
Cardiac CT perfusion imaging of pericoronary adipose tissue (PCAT) highlights potential confounds in coronary CTA
HU, blood flow, and radiomics were assessed over time.
Self-supervised Learning by View Synthesis
In each iteration, the input to VSA is one view (or multiple views) of a 3D object and the output is a synthesized image in another target pose.
Point2Pix: Photo-Realistic Point Cloud Rendering via Neural Radiance Fields
Also, we present Point Encoding to build Multi-scale Radiance Fields that provide discriminative 3D point features.
TriVol: Point Cloud Rendering via Triple Volumes
However, artifacts still appear in rendered images, due to the challenges in extracting continuous and discriminative 3D features from point clouds.
Ref-NeuS: Ambiguity-Reduced Neural Implicit Surface Learning for Multi-View Reconstruction with Reflection
We show that together with a reflection direction-dependent radiance, our model achieves high-quality surface reconstruction on reflective surfaces and outperforms the state-of-the-arts by a large margin.
NeRFLiX: High-Quality Neural View Synthesis by Learning a Degradation-Driven Inter-viewpoint MiXer
Neural radiance fields (NeRF) show great success in novel view synthesis.
Ranked #1 on
Novel View Synthesis
on LLFF
EfficientNeRF: Efficient Neural Radiance Fields
Neural Radiance Fields (NeRF) has been wildly applied to various tasks for its high-quality representation of 3D scenes.
Human-Object Interaction Detection via Disentangled Transformer
To associate the predictions of disentangled decoders, we first generate a unified representation for HOI triplets with a base decoder, and then utilize it as input feature of each disentangled decoder.
EfficientNeRF Efficient Neural Radiance Fields
Neural Radiance Fields (NeRF) has been wildly applied to various tasks for its high-quality representation of 3D scenes.
HVTR: Hybrid Volumetric-Textural Rendering for Human Avatars
To handle complicated motions (e. g., self-occlusions), we then leverage the encoded information on the UV manifold to construct a 3D volumetric representation based on a dynamic pose-conditioned neural radiance field.
GaTector: A Unified Framework for Gaze Object Prediction
In this paper, we build a novel framework named GaTector to tackle the gaze object prediction problem in a unified way.
EgoRenderer: Rendering Human Avatars from Egocentric Camera Images
We next combine the target pose image and the textures into a combined feature image, which is transformed into the output color image using a neural image translation network.
CRD-CGAN: Category-Consistent and Relativistic Constraints for Diverse Text-to-Image Generation
Based on those constraints, a category-consistent and relativistic diverse conditional GAN (CRD-CGAN) is proposed to synthesize $K$ photo-realistic images simultaneously.
Self-Supervised 3D Mesh Reconstruction From Single Images
Recent single-view 3D reconstruction methods reconstruct object's shape and texture from a single image with only 2D image-level annotation.
Exploring the impact of under-reported cases on the COVID-19 spatiotemporal distribution using healthcare worker infection data
Therefore, in this paper, a novel framework was proposed to explore the impact of under-reporting on COVID-19 spatiotemporal distributions, and empirical analysis was carried out using infection data of healthcare workers in Wuhan and Hubei (excluding Wuhan).
Hierarchical Modes Exploring in Generative Adversarial Networks
In conditional Generative Adversarial Networks (cGANs), when two different initial noises are concatenated with the same conditional information, the distance between their outputs is relatively smaller, which makes minor modes likely to collapse into large modes.
Multi-object Tracking via End-to-end Tracklet Searching and Ranking
Recent works in multiple object tracking use sequence model to calculate the similarity score between the detections and the previous tracklets.
Learning to Generate Dense Point Clouds with Textures on Multiple Categories
In this paper, we propose a novel approach for reconstructing point clouds from RGB images.
3D Shape Completion with Multi-view Consistent Inference
We formulate the regularization term as a consistency loss that encourages geometric consistency among multiple views, while the data term guarantees that the optimized views do not drift away too much from a learned shape descriptor.
SILCO: Show a Few Images, Localize the Common Object
To that end, we introduce a spatial similarity module that searches the spatial commonality among the given images.
A Radio Signal Modulation Recognition Algorithm Based on Residual Networks and Attention Mechanisms
To solve the problem of inaccurate recognition of types of communication signal modulation, a RNN neural network recognition algorithm combining residual block network with attention mechanism is proposed.
Real Time Visual Tracking using Spatial-Aware Temporal Aggregation Network
Our tracker achieves leading performance in OTB2013, OTB2015, VOT2015, VOT2016 and LaSOT, and operates at a real-time speed of 26 FPS, which indicates our method is effective and practical.
VITAL: A Visual Interpretation on Text with Adversarial Learning for Image Labeling
In this paper, we propose a novel way to interpret text information by extracting visual feature presentation from multiple high-resolution and photo-realistic synthetic images generated by Text-to-image Generative Adversarial Network (GAN) to improve the performance of image labeling.
Attention-Based Multi-Context Guiding for Few-Shot Semantic Segmentation
Few-shot learning is a nascent research topic, motivated by the fact that traditional deep learning methods require tremen- dous amounts of data.
Ranked #1 on
Few-Shot Semantic Segmentation
on Pascal5i
Render4Completion: Synthesizing Multi-View Depth Maps for 3D Shape Completion
Different from image-to-image translation network that completes each view separately, our novel network, multi-view completion net (MVCN), leverages information from all views of a 3D shape to help the completion of each single view.
See Better Before Looking Closer: Weakly Supervised Data Augmentation Network for Fine-Grained Visual Classification
Specifically, for each training image, we first generate attention maps to represent the object's discriminative parts by weakly supervised learning.
Ranked #12 on
Fine-Grained Image Classification
on CUB-200-2011
Weakly Supervised Bilinear Attention Network for Fine-Grained Visual Classification
Besides, we propose attention regularization and attention dropout to weakly supervise the generating process of attention maps.
Facial Landmarks Detection by Self-Iterative Regression based Landmarks-Attention Network
Only one self-iterative regressor is trained to learn the descent directions for samples from coarse stages to fine stages, and parameters are iteratively updated by the same regressor.
Ranked #16 on
Face Alignment
on 300W
(NME_inter-pupil (%, Common) metric)
A Hebbian/Anti-Hebbian Neural Network for Linear Subspace Learning: A Derivation from Multidimensional Scaling of Streaming Data
Such networks learn the principal subspace, in the sense of principal component analysis (PCA), by adjusting synaptic weights according to activity-dependent learning rules.
A Hebbian/Anti-Hebbian Network for Online Sparse Dictionary Learning Derived from Symmetric Matrix Factorization
Here, to overcome this problem, we derive sparse dictionary learning from a novel cost-function - a regularized error of the symmetric factorization of the input's similarity matrix.
Task-group Relatedness and Generalization Bounds for Regularized Multi-task Learning
We are mainly concerned with two theoretical questions: 1) under what conditions does RMTL perform better with a smaller task sample size than STL?
A Neuron as a Signal Processing Device
Here we propose to view a neuron as a signal processing device that represents the incoming streaming data matrix as a sparse vector of synaptic weights scaled by an outgoing sparse activity vector.
A mechanistic model of early sensory processing based on subtracting sparse representations
However, feedback inhibitory circuits are common in early sensory circuits and furthermore their dynamics may be nonlinear.
Reconstruction of Sparse Circuits Using Multi-neuronal Excitation (RESCUME)
One of the central problems in neuroscience is reconstructing synaptic connectivity in neural circuits.
