Search Results for author:
Found 105 papers, 31 papers with code
Unveiling Advanced Frequency Disentanglement Paradigm for Low-Light Image Enhancement
Previous low-light image enhancement (LLIE) approaches, while employing frequency decomposition techniques to address the intertwined challenges of low frequency (e. g., illumination recovery) and high frequency (e. g., noise reduction), primarily focused on the development of dedicated and complex networks to achieve improved performance.
Towards Realistic Example-based Modeling via 3D Gaussian Stitching
To this end, we present an example-based modeling method that combines multiple Gaussian fields in a point-based representation using sample-guided synthesis.
CT4D: Consistent Text-to-4D Generation with Animatable Meshes
The primary challenges of our mesh-based framework involve stably generating a mesh with details that align with the text prompt while directly driving it and maintaining surface continuity.
ViMo: Generating Motions from Casual Videos
Although humans have the innate ability to imagine multiple possible actions from videos, it remains an extraordinary challenge for computers due to the intricate camera movements and montages.
DreamDissector: Learning Disentangled Text-to-3D Generation from 2D Diffusion Priors
To achieve this, we introduce the Neural Category Field (NeCF) for disentangling the input NeRF.
ToDER: Towards Colonoscopy Depth Estimation and Reconstruction with Geometry Constraint Adaptation
Furthermore, we carefully design a TNet module in our adaptation architecture to yield geometry constraints and obtain better depth quality.
GaussReg: Fast 3D Registration with Gaussian Splatting
The existing methods usually convert the implicit representation to explicit representation for further registration.
StableNormal: Reducing Diffusion Variance for Stable and Sharp Normal
The effectiveness of StableNormal is demonstrated through competitive performance in standard datasets such as DIODE-indoor, iBims, ScannetV2 and NYUv2, and also in various downstream tasks, such as surface reconstruction and normal enhancement.
Sketch2Human: Deep Human Generation with Disentangled Geometry and Appearance Control
This work presents Sketch2Human, the first system for controllable full-body human image generation guided by a semantic sketch (for geometry control) and a reference image (for appearance control).
SphereHead: Stable 3D Full-head Synthesis with Spherical Tri-plane Representation
We further introduce a view-image consistency loss for the discriminator to emphasize the correspondence of the camera parameters and the images.
GauStudio: A Modular Framework for 3D Gaussian Splatting and Beyond
We present GauStudio, a novel modular framework for modeling 3D Gaussian Splatting (3DGS) to provide standardized, plug-and-play components for users to easily customize and implement a 3DGS pipeline.
IPoD: Implicit Field Learning with Point Diffusion for Generalizable 3D Object Reconstruction from Single RGB-D Images
This approach treats the query points for implicit field learning as a noisy point cloud for iterative denoising allowing for their dynamic adaptation to the target object shape.
LASA: Instance Reconstruction from Real Scans using A Large-scale Aligned Shape Annotation Dataset
Instance shape reconstruction from a 3D scene involves recovering the full geometries of multiple objects at the semantic instance level.
PICTURE: PhotorealistIC virtual Try-on from UnconstRained dEsigns
Unlike prior arts constrained by specific input types, our method allows flexible specification of style (text or image) and texture (full garment, cropped sections, or texture patches) conditions.
MVHumanNet: A Large-scale Dataset of Multi-view Daily Dressing Human Captures
In this era, the success of large language models and text-to-image models can be attributed to the driving force of large-scale datasets.
SAMPro3D: Locating SAM Prompts in 3D for Zero-Shot Scene Segmentation
We introduce SAMPro3D for zero-shot 3D indoor scene segmentation.
RichDreamer: A Generalizable Normal-Depth Diffusion Model for Detail Richness in Text-to-3D
Lifting 2D diffusion for 3D generation is a challenging problem due to the lack of geometric prior and the complex entanglement of materials and lighting in natural images.
HAVE-FUN: Human Avatar Reconstruction from Few-Shot Unconstrained Images
As for human avatar reconstruction, contemporary techniques commonly necessitate the acquisition of costly data and struggle to achieve satisfactory results from a small number of casual images.
FIRST: A Million-Entry Dataset for Text-Driven Fashion Synthesis and Design
Text-driven fashion synthesis and design is an extremely valuable part of artificial intelligence generative content(AIGC), which has the potential to propel a tremendous revolution in the traditional fashion industry.
SeamlessNeRF: Stitching Part NeRFs with Gradient Propagation
To fill this gap, we propose SeamlessNeRF, a novel approach for seamless appearance blending of multiple NeRFs.
Activate and Reject: Towards Safe Domain Generalization under Category Shift
Albeit the notable performance on in-domain test points, it is non-trivial for deep neural networks to attain satisfactory accuracy when deploying in the open world, where novel domains and object classes often occur.
EMS: 3D Eyebrow Modeling from Single-view Images
Three modules are then carefully designed: RootFinder firstly localizes the fiber root positions which indicates where to grow; OriPredictor predicts an orientation field in the 3D space to guide the growing of fibers; FiberEnder is designed to determine when to stop the growth of each fiber.
Efficient View Synthesis with Neural Radiance Distribution Field
Specifically, we use a small network similar to NeRF while preserving the rendering speed with a single network forwarding per pixel as in NeLF.
Free-ATM: Exploring Unsupervised Learning on Diffusion-Generated Images with Free Attention Masks
Despite the rapid advancement of unsupervised learning in visual representation, it requires training on large-scale datasets that demand costly data collection, and pose additional challenges due to concerns regarding data privacy.
Universal Semi-supervised Model Adaptation via Collaborative Consistency Training
In this paper, we introduce a realistic and challenging domain adaptation problem called Universal Semi-supervised Model Adaptation (USMA), which i) requires only a pre-trained source model, ii) allows the source and target domain to have different label sets, i. e., they share a common label set and hold their own private label set, and iii) requires only a few labeled samples in each class of the target domain.
SketchMetaFace: A Learning-based Sketching Interface for High-fidelity 3D Character Face Modeling
User studies demonstrate the superiority of our system over existing modeling tools in terms of the ease to use and visual quality of results.
3D Keypoint Estimation Using Implicit Representation Learning
Inspired by the recent success of advanced implicit representation in reconstruction tasks, we explore the idea of using an implicit field to represent keypoints.
AUGUST: an Automatic Generation Understudy for Synthesizing Conversational Recommendation Datasets
High-quality data is essential for conversational recommendation systems and serves as the cornerstone of the network architecture development and training strategy design.
From NeRFLiX to NeRFLiX++: A General NeRF-Agnostic Restorer Paradigm
To address this, we propose NeRFLiX, a general NeRF-agnostic restorer paradigm that learns a degradation-driven inter-viewpoint mixer.
Ranked #1 on
Novel View Synthesis
on Tanks and Temples
REC-MV: REconstructing 3D Dynamic Cloth from Monocular Videos
To address the above limitations, in this paper, we formulate this task as an optimization problem of 3D garment feature curves and surface reconstruction from monocular video.
FashionTex: Controllable Virtual Try-on with Text and Texture
Virtual try-on attracts increasing research attention as a promising way for enhancing the user experience for online cloth shopping.
SCoDA: Domain Adaptive Shape Completion for Real Scans
Thus, we propose a new task, SCoDA, for the domain adaptation of real scan shape completion from synthetic data.
NerVE: Neural Volumetric Edges for Parametric Curve Extraction from Point Cloud
We show that a simple network based on NerVE can already outperform the previous state-of-the-art methods by a great margin.
RaBit: Parametric Modeling of 3D Biped Cartoon Characters with a Topological-consistent Dataset
However, none of the prior works focus on modeling 3D biped cartoon characters, which are also in great demand in gaming and filming.
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
MVImgNet: A Large-scale Dataset of Multi-view Images
The birth of ImageNet drives a remarkable trend of "learning from large-scale data" in computer vision.
HairStep: Transfer Synthetic to Real Using Strand and Depth Maps for Single-View 3D Hair Modeling
We firmly think an intermediate representation is essential, but we argue that orientation map using the dominant filtering-based methods is sensitive to uncertain noise and far from a competent representation.
Get3DHuman: Lifting StyleGAN-Human into a 3D Generative Model using Pixel-aligned Reconstruction Priors
Specifically, we bridge the latent space of Get3DHuman with that of StyleGAN-Human via a specially-designed prior network, where the input latent code is mapped to the shape and texture feature volumes spanned by the pixel-aligned 3D reconstructor.
RecolorNeRF: Layer Decomposed Radiance Fields for Efficient Color Editing of 3D Scenes
We present RecolorNeRF, a novel user-friendly color editing approach for the neural radiance fields.
MM-3DScene: 3D Scene Understanding by Customizing Masked Modeling with Informative-Preserved Reconstruction and Self-Distilled Consistency
Besides, such scenes with progressive masking ratios can also serve to self-distill their intrinsic spatial consistency, requiring to learn the consistent representations from unmasked areas.
Which Pixel to Annotate: a Label-Efficient Nuclei Segmentation Framework
Recently deep neural networks, which require a large amount of annotated samples, have been widely applied in nuclei instance segmentation of H\&E stained pathology images.
MIMO Is All You Need : A Strong Multi-In-Multi-Out Baseline for Video Prediction
The mainstream of the existing approaches for video prediction builds up their models based on a Single-In-Single-Out (SISO) architecture, which takes the current frame as input to predict the next frame in a recursive manner.
Mutual Guidance and Residual Integration for Image Enhancement
To address those issues, we propose a novel mutual guidance network (MGN) to perform effective bidirectional global-local information exchange while keeping a compact architecture.
Learning 3D Scene Priors with 2D Supervision
Holistic 3D scene understanding entails estimation of both layout configuration and object geometry in a 3D environment.
Point Cloud Scene Completion with Joint Color and Semantic Estimation from Single RGB-D Image
Given a single RGB-D image, our method first predicts its semantic segmentation map and goes through the 3D volume branch to obtain a volumetric scene reconstruction as a guide to the next view inpainting step, which attempts to make up the missing information; the third step involves projecting the volume under the same view of the input, concatenating them to complete the current view RGB-D and segmentation map, and integrating all RGB-D and segmentation maps into the point cloud.
A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspective
Graph Neural Networks (GNNs) have gained momentum in graph representation learning and boosted the state of the art in a variety of areas, such as data mining (\emph{e. g.,} social network analysis and recommender systems), computer vision (\emph{e. g.,} object detection and point cloud learning), and natural language processing (\emph{e. g.,} relation extraction and sequence learning), to name a few.
PIFu for the Real World: A Self-supervised Framework to Reconstruct Dressed Human from Single-view Images
On synthetic data, our Intersection-Over-Union (IoU) achieves to 93. 5%, 18% higher compared with PIFuHD.
Towards High-Fidelity Single-view Holistic Reconstruction of Indoor Scenes
We present a new framework to reconstruct holistic 3D indoor scenes including both room background and indoor objects from single-view images.
Relation Matters: Foreground-aware Graph-based Relational Reasoning for Domain Adaptive Object Detection
However, both the global and local alignment approaches fail to capture the topological relations among different foreground objects as the explicit dependencies and interactions between and within domains are neglected.
Multi-level Consistency Learning for Semi-supervised Domain Adaptation
Semi-supervised domain adaptation (SSDA) aims to apply knowledge learned from a fully labeled source domain to a scarcely labeled target domain.
Ranked #1 on
Semi-supervised Domain Adaptation
on VisDA2017
DArch: Dental Arch Prior-assisted 3D Tooth Instance Segmentation
Experimental results on $4, 773$ dental models have shown our DArch can accurately segment each tooth of a dental model, and its performance is superior to the state-of-the-art methods.
Registering Explicit to Implicit: Towards High-Fidelity Garment mesh Reconstruction from Single Images
Fueled by the power of deep learning techniques and implicit shape learning, recent advances in single-image human digitalization have reached unprecedented accuracy and could recover fine-grained surface details such as garment wrinkles.
SharpContour: A Contour-based Boundary Refinement Approach for Efficient and Accurate Instance Segmentation
Contour-based models are efficient and generic to be incorporated with any existing segmentation methods, but they often generate over-smoothed contour and tend to fail on corner areas.
Compound Domain Generalization via Meta-Knowledge Encoding
Such holistic semantic structure, referred to as meta-knowledge here, is crucial for learning generalizable representations.
Exploring Motion Ambiguity and Alignment for High-Quality Video Frame Interpolation
Without the bells and whistles, our plug-and-play TCL is capable of improving the performance of existing VFI frameworks.
Ranked #1 on
Video Frame Interpolation
on Vimeo90K
TO-Scene: A Large-scale Dataset for Understanding 3D Tabletop Scenes
Experiments show that the algorithms trained on TO-Scene indeed work on the realistic test data, and our proposed tabletop-aware learning strategy greatly improves the state-of-the-art results on both 3D semantic segmentation and object detection tasks.
Real-World Blind Super-Resolution via Feature Matching with Implicit High-Resolution Priors
Unlike image-space methods, our FeMaSR restores HR images by matching distorted LR image {\it features} to their distortion-free HR counterparts in our pretrained HR priors, and decoding the matched features to obtain realistic HR images.
PointMatch: A Consistency Training Framework for Weakly Supervised Semantic Segmentation of 3D Point Clouds
Semantic segmentation of point cloud usually relies on dense annotation that is exhausting and costly, so it attracts wide attention to investigate solutions for the weakly supervised scheme with only sparse points annotated.
Representation Learning
Weakly supervised Semantic Segmentation
+1
PVSeRF: Joint Pixel-, Voxel- and Surface-Aligned Radiance Field for Single-Image Novel View Synthesis
We present PVSeRF, a learning framework that reconstructs neural radiance fields from single-view RGB images, for novel view synthesis.
Expressive Talking Head Generation With Granular Audio-Visual Control
Generating expressive talking heads is essential for creating virtual humans.
DArch: Dental Arch Prior-Assisted 3D Tooth Instance Segmentation With Weak Annotations
Experimental results on 4, 773 dental models have shown our DArch can accurately segment each tooth of a dental model, and its performance is superior to the state-of-the-art methods.
ETHSeg: An Amodel Instance Segmentation Network and a Real-World Dataset for X-Ray Waste Inspection
Inspired by the fact that X-ray has a strong penetrating power to see through the bag and overlapping objects, we propose to perform waste inspection efficiently using X-ray images without the need to open the bag.
Pose2Room: Understanding 3D Scenes from Human Activities
To this end, we propose P2R-Net to learn a probabilistic 3D model of the objects in a scene characterized by their class categories and oriented 3D bounding boxes, based on an input observed human trajectory in the environment.
Revisiting Temporal Alignment for Video Restoration
Long-range temporal alignment is critical yet challenging for video restoration tasks.
Ranked #1 on
Video Super-Resolution
on Vimeo-90K
Pixel-level Intra-domain Adaptation for Semantic Segmentation
Recent advances in unsupervised domain adaptation have achieved remarkable performance on semantic segmentation tasks.
SketchHairSalon: Deep Sketch-based Hair Image Synthesis
At the second stage, another network is trained to synthesize the structure and appearance of hair images from the input sketch and the generated matte.
Preservational Learning Improves Self-supervised Medical Image Models by Reconstructing Diverse Contexts
From this perspective, we introduce Preservational Learning to reconstruct diverse image contexts in order to preserve more information in learned representations.
ME-PCN: Point Completion Conditioned on Mask Emptiness
Main-stream methods predict the missing shapes by decoding a global feature learned from the input point cloud, which often leads to deficient results in preserving topology consistency and surface details.
SimpModeling: Sketching Implicit Field to Guide Mesh Modeling for 3D Animalmorphic Head Design
In this work, we propose SimpModeling, a novel sketch-based system for helping users, especially amateur users, easily model 3D animalmorphic heads - a prevalent kind of heads in character design.
From Single to Multiple: Leveraging Multi-level Prediction Spaces for Video Forecasting
Despite video forecasting has been a widely explored topic in recent years, the mainstream of the existing work still limits their models with a single prediction space but completely neglects the way to leverage their model with multi-prediction spaces.
Transformer with Peak Suppression and Knowledge Guidance for Fine-grained Image Recognition
In this paper, we analyze the difficulties of fine-grained image recognition from a new perspective and propose a transformer architecture with the peak suppression module and knowledge guidance module, which respects the diversification of discriminative features in a single image and the aggregation of discriminative clues among multiple images.
Ranked #7 on
Fine-Grained Image Classification
on Stanford Dogs
Fine-Grained Image Classification
Fine-Grained Image Recognition
Task-Aware Sampling Layer for Point-Wise Analysis
Sampling, grouping, and aggregation are three important components in the multi-scale analysis of point clouds.
Hepatocellular Carcinoma Segmentation from Digital Subtraction Angiography Videos using Learnable Temporal Difference
In this paper, we raise the problem of HCC segmentation in DSA videos, and build our own DSA dataset.
3DCaricShop: A Dataset and A Baseline Method for Single-view 3D Caricature Face Reconstruction
Caricature is an artistic representation that deliberately exaggerates the distinctive features of a human face to convey humor or sarcasm.
Refer-it-in-RGBD: A Bottom-up Approach for 3D Visual Grounding in RGBD Images
Grounding referring expressions in RGBD image has been an emerging field.
LapsCore: Language-Guided Person Search via Color Reasoning
The key point of language-guided person search is to construct the cross-modal association between visual and textual input.
RfD-Net: Point Scene Understanding by Semantic Instance Reconstruction
In this work, we introduce RfD-Net that jointly detects and reconstructs dense object surfaces directly from raw point clouds.
JOLO-GCN: Mining Joint-Centered Light-Weight Information for Skeleton-Based Action Recognition
Skeleton-based action recognition has attracted research attentions in recent years.
Skeleton-bridged Point Completion: From Global Inference to Local Adjustment
Existing works usually estimate the missing shape by decoding a latent feature encoded from the input points.
A deep learning based interactive sketching system for fashion images design
In this work, we propose an interactive system to design diverse high-quality garment images from fashion sketches and the texture information.
Reinforcement Learning for Weakly Supervised Temporal Grounding of Natural Language in Untrimmed Videos
Temporal grounding of natural language in untrimmed videos is a fundamental yet challenging multimedia task facilitating cross-media visual content retrieval.
Ultrasound Liver Fibrosis Diagnosis using Multi-indicator guided Deep Neural Networks
An indicator-guided learning mechanism is further proposed to ease the training of the proposed model.
SkeletonNet: A Topology-Preserving Solution for Learning Mesh Reconstruction of Object Surfaces from RGB Images
However, they all have their own drawbacks, and cannot properly reconstruct the surface shapes of complex topologies, arguably due to a lack of constraints on the topologicalstructures in their learning frameworks.
Deep Fashion3D: A Dataset and Benchmark for 3D Garment Reconstruction from Single Images
High-fidelity clothing reconstruction is the key to achieving photorealism in a wide range of applications including human digitization, virtual try-on, etc.
Learning Inverse Rendering of Faces from Real-world Videos
To alleviate this problem, we propose a weakly supervised training approach to train our model on real face videos, based on the assumption of consistency of albedo and normal across different frames, thus bridging the gap between real and synthetic face images.
Peeking into occluded joints: A novel framework for crowd pose estimation
Although occlusion widely exists in nature and remains a fundamental challenge for pose estimation, existing heatmap-based approaches suffer serious degradation on occlusions.
HEMlets PoSh: Learning Part-Centric Heatmap Triplets for 3D Human Pose and Shape Estimation
Estimating 3D human pose from a single image is a challenging task.
Total3DUnderstanding: Joint Layout, Object Pose and Mesh Reconstruction for Indoor Scenes from a Single Image
Semantic reconstruction of indoor scenes refers to both scene understanding and object reconstruction.
Ranked #2 on
3D Shape Reconstruction
on Pix3D
FPConv: Learning Local Flattening for Point Convolution
We introduce FPConv, a novel surface-style convolution operator designed for 3D point cloud analysis.
Shallow2Deep: Indoor Scene Modeling by Single Image Understanding
Particularly, we design a shallow-to-deep architecture on the basis of convolutional networks for semantic scene understanding and modeling.
Self-Enhanced Convolutional Network for Facial Video Hallucination
It further recurrently exploits the reconstructed results and intermediate features of a sequence of preceding frames to improve the initial super-resolution of the current frame by modelling the coherence of structural facial features across frames.
HEMlets Pose: Learning Part-Centric Heatmap Triplets for Accurate 3D Human Pose Estimation
Estimating 3D human pose from a single image is a challenging task.
Ranked #1 on
Monocular 3D Human Pose Estimation
on Human3.6M
(Use Video Sequence metric, using extra
training data)
Deep Mesh Reconstruction from Single RGB Images via Topology Modification Networks
The key to our approach is a novel progressive shaping framework that alternates between mesh deformation and topology modification.
Ranked #3 on
3D Shape Reconstruction
on Pix3D
A Skeleton-bridged Deep Learning Approach for Generating Meshesof Complex Topologies from Single RGB Image
To this end, we propose in this paper a skeleton-bridged, stage-wise learning approach to address the challenge.
A Skeleton-bridged Deep Learning Approach for Generating Meshes of Complex Topologies from Single RGB Images
To this end, we propose in this paper a skeleton-bridged, stage-wise learning approach to address the challenge.
Deep Reinforcement Learning of Volume-guided Progressive View Inpainting for 3D Point Scene Completion from a Single Depth Image
Given a single depth image, our method first goes through the 3D volume branch to obtain a volumetric scene reconstruction as a guide to the next view inpainting step, which attempts to make up the missing information; the third step involves projecting the volume under the same view of the input, concatenating them to complete the current view depth, and integrating all depth into the point cloud.
Two-phase Hair Image Synthesis by Self-Enhancing Generative Model
Generating plausible hair image given limited guidance, such as sparse sketches or low-resolution image, has been made possible with the rise of Generative Adversarial Networks (GANs).
Learning Mutually Local-global U-nets For High-resolution Retinal Lesion Segmentation in Fundus Images
Due to high-resolution and small-size lesion regions, applying existing methods, such as U-Nets, to perform segmentation on fundus photography is very challenging.
Deep RBFNet: Point Cloud Feature Learning using Radial Basis Functions
Three-dimensional object recognition has recently achieved great progress thanks to the development of effective point cloud-based learning frameworks, such as PointNet and its extensions.
Adversarial 3D Human Pose Estimation via Multimodal Depth Supervision
In this paper, a novel deep-learning based framework is proposed to infer 3D human poses from a single image.
CaricatureShop: Personalized and Photorealistic Caricature Sketching
To construct the mapping between 2D sketches and a vertex-wise scaling field, a novel deep learning architecture is developed.
FBI-Pose: Towards Bridging the Gap between 2D Images and 3D Human Poses using Forward-or-Backward Information
Although significant advances have been made in the area of human poses estimation from images using deep Convolutional Neural Network (ConvNet), it remains a big challenge to perform 3D pose inference in-the-wild.
Ranked #243 on
3D Human Pose Estimation
on Human3.6M
Video Inpainting by Jointly Learning Temporal Structure and Spatial Details
We present a new data-driven video inpainting method for recovering missing regions of video frames.
High-Resolution Shape Completion Using Deep Neural Networks for Global Structure and Local Geometry Inference
Our method is based on a new deep learning architecture consisting of two sub-networks: a global structure inference network and a local geometry refinement network.
DeepSketch2Face: A Deep Learning Based Sketching System for 3D Face and Caricature Modeling
This system has a labor-efficient sketching interface, that allows the user to draw freehand imprecise yet expressive 2D lines representing the contours of facial features.
