Search Results for author:
Found 36 papers, 18 papers with code
CAT-3DGS: A Context-Adaptive Triplane Approach to Rate-Distortion-Optimized 3DGS Compression
But, they fail to utilize fully the inter and intra correlation inherent in Gaussian primitives.
GCC: Generative Color Constancy via Diffusing a Color Checker
Color constancy methods often struggle to generalize across different camera sensors due to varying spectral sensitivities.
AuraFusion360: Augmented Unseen Region Alignment for Reference-based 360° Unbounded Scene Inpainting
Three-dimensional scene inpainting is crucial for applications from virtual reality to architectural visualization, yet existing methods struggle with view consistency and geometric accuracy in 360{\deg} unbounded scenes.
CorrFill: Enhancing Faithfulness in Reference-based Inpainting with Correspondence Guidance in Diffusion Models
In the task of reference-based image inpainting, an additional reference image is provided to restore a damaged target image to its original state.
ORFormer: Occlusion-Robust Transformer for Accurate Facial Landmark Detection
The messenger token aggregates features from all but its patch.
ReF-LDM: A Latent Diffusion Model for Reference-based Face Image Restoration
While recent works on blind face image restoration have successfully produced impressive high-quality (HQ) images with abundant details from low-quality (LQ) input images, the generated content may not accurately reflect the real appearance of a person.
FIPER: Generalizable Factorized Features for Robust Low-Level Vision Models
In this work, we propose using a unified representation, termed Factorized Features, for low-level vision tasks, where we test on Single Image Super-Resolution (SISR) and Image Compression.
SpectroMotion: Dynamic 3D Reconstruction of Specular Scenes
We present SpectroMotion, a novel approach that combines 3D Gaussian Splatting (3DGS) with physically-based rendering (PBR) and deformation fields to reconstruct dynamic specular scenes.
FrugalNeRF: Fast Convergence for Few-shot Novel View Synthesis without Learned Priors
Neural Radiance Fields (NeRF) face significant challenges in extreme few-shot scenarios, primarily due to overfitting and long training times.
Precise Pick-and-Place using Score-Based Diffusion Networks
In this paper, we propose a novel coarse-to-fine continuous pose diffusion method to enhance the precision of pick-and-place operations within robotic manipulation tasks.
Matting by Generation
This paper introduces an innovative approach for image matting that redefines the traditional regression-based task as a generative modeling challenge.
BoostMVSNeRFs: Boosting MVS-based NeRFs to Generalizable View Synthesis in Large-scale Scenes
This paper presents a novel approach called BoostMVSNeRFs to enhance the rendering quality of MVS-based NeRFs in large-scale scenes.
GenRC: Generative 3D Room Completion from Sparse Image Collections
GenRC outperforms state-of-the-art methods under most appearance and geometric metrics on ScanNet and ARKitScenes datasets, even though GenRC is not trained on these datasets nor using predefined camera trajectories.
DiffIR2VR-Zero: Zero-Shot Video Restoration with Diffusion-based Image Restoration Models
This paper introduces a method for zero-shot video restoration using pre-trained image restoration diffusion models.
Depth Anywhere: Enhancing 360 Monocular Depth Estimation via Perspective Distillation and Unlabeled Data Augmentation
Our approach uses state-of-the-art perspective depth estimation models as teacher models to generate pseudo labels through a six-face cube projection technique, enabling efficient labeling of depth in 360-degree images.
NaRCan: Natural Refined Canonical Image with Integration of Diffusion Prior for Video Editing
We propose a video editing framework, NaRCan, which integrates a hybrid deformation field and diffusion prior to generate high-quality natural canonical images to represent the input video.
DeNVeR: Deformable Neural Vessel Representations for Unsupervised Video Vessel Segmentation
This paper presents Deformable Neural Vessel Representations (DeNVeR), an unsupervised approach for vessel segmentation in X-ray angiography videos without annotated ground truth.
Image-Text Co-Decomposition for Text-Supervised Semantic Segmentation
This paper addresses text-supervised semantic segmentation, aiming to learn a model capable of segmenting arbitrary visual concepts within images by using only image-text pairs without dense annotations.
Improving Robustness for Joint Optimization of Camera Poses and Decomposed Low-Rank Tensorial Radiance Fields
In this paper, we propose an algorithm that allows joint refinement of camera pose and scene geometry represented by decomposed low-rank tensor, using only 2D images as supervision.
HumanNeRF-SE: A Simple yet Effective Approach to Animate HumanNeRF with Diverse Poses
Our architecture involving both explicit and implicit representation is simple yet effective.
Learning Continuous Exposure Value Representations for Single-Image HDR Reconstruction
To address this, we propose the continuous exposure value representation (CEVR), which uses an implicit function to generate LDR images with arbitrary EVs, including those unseen during training.
ImGeoNet: Image-induced Geometry-aware Voxel Representation for Multi-view 3D Object Detection
The results demonstrate that ImGeoNet outperforms the current state-of-the-art multi-view image-based method, ImVoxelNet, on all three datasets in terms of detection accuracy.
Ranked #29 on
3D Object Detection
on ScanNetV2
Dual Associated Encoder for Face Restoration
Restoring facial details from low-quality (LQ) images has remained a challenging problem due to its ill-posedness induced by various degradations in the wild.
Ranked #2 on
Blind Face Restoration
on WIDER
Progressively Optimized Local Radiance Fields for Robust View Synthesis
For handling unknown poses, we jointly estimate the camera poses with radiance field in a progressive manner.
DisCO: Portrait Distortion Correction with Perspective-Aware 3D GANs
Close-up facial images captured at short distances often suffer from perspective distortion, resulting in exaggerated facial features and unnatural/unattractive appearances.
Robust Dynamic Radiance Fields
Dynamic radiance field reconstruction methods aim to model the time-varying structure and appearance of a dynamic scene.
Denoising Likelihood Score Matching for Conditional Score-based Data Generation
These methods facilitate the training procedure of conditional score models, as a mixture of scores can be separately estimated using a score model and a classifier.
Ranked #1 on
Conditional Image Generation
on CIFAR-100
Bridging Unsupervised and Supervised Depth from Focus via All-in-Focus Supervision
In this paper, we propose a method to estimate not only a depth map but an AiF image from a set of images with different focus positions (known as a focal stack).
Hybrid Neural Fusion for Full-frame Video Stabilization
Existing video stabilization methods often generate visible distortion or require aggressive cropping of frame boundaries, resulting in smaller field of views.
Explorable Tone Mapping Operators
It aims to preserve visual information of HDR images in a medium with a limited dynamic range.
Learning Camera-Aware Noise Models
Modeling imaging sensor noise is a fundamental problem for image processing and computer vision applications.
Learning to See Through Obstructions with Layered Decomposition
We present a learning-based approach for removing unwanted obstructions, such as window reflections, fence occlusions, or adherent raindrops, from a short sequence of images captured by a moving camera.
Single-Image HDR Reconstruction by Learning to Reverse the Camera Pipeline
We model the HDRto-LDR image formation pipeline as the (1) dynamic range clipping, (2) non-linear mapping from a camera response function, and (3) quantization.
Learning to See Through Obstructions
We present a learning-based approach for removing unwanted obstructions, such as window reflections, fence occlusions or raindrops, from a short sequence of images captured by a moving camera.
Attention-based View Selection Networks for Light-field Disparity Estimation
This paper introduces a novel deep network for estimating depth maps from a light field image.
Ranked #1 on
Depth Estimation
on 4D Light Field Dataset
Deep Video Frame Interpolation using Cyclic Frame Generation
In addition to the cycle consistency loss, we propose two extensions: motion linearity loss and edge-guided training.
