3 citations · 5 across the 14 of their papers we have counts for
14 papers
CamFlow+: Hybrid Motion Bases for 2D Camera Motion Estimation with Stabilization Applications
Haipeng Li, Zhen Liu, Zhanglei Yang +6
Estimating 2D camera motion is fundamental to computer vision and computational photography. Existing homography-based methods work well for planar scenes or pure rotation, but str…
ZeroIDIR: Zero-Reference Illumination Degradation Image Restoration with Perturbed Consistency Diffusion Models
Hai Jiang, Zhen Liu, Yinjie Lei +3
In this paper, we propose a zero-reference diffusion-based framework, named ZeroIDIR, for illumination degradation image restoration, which decouples the restoration process into a…
ExpoCM: Exposure-Aware One-Step Generative Single-Image HDR Reconstruction
Aoyu Liu, Zhen Liu, Ziyi Wang +3
Single-image HDR reconstruction aims to recover high dynamic range radiance from a single low dynamic range (LDR) input, but remains highly ill-posed due to detail saturation in ov…
DMAligner: Enhancing Image Alignment via Diffusion Model Based View Synthesis
Xinglong Luo, Ao Luo, Zhengning Wang +5
Image alignment is a fundamental task in computer vision with broad applications. Existing methods predominantly employ optical flow-based image warping. However, this technique is…
Phys-3D: Physics-Constrained Real-Time Crowd Tracking and Counting on Railway Platforms
Bin Zeng, Johannes Künzel, Anna Hilsmann +1
Accurate, real-time crowd counting on railway platforms is essential for safety and capacity management. We propose to use a single camera mounted in a train, scanning the platform…
RAW-Flow: Advancing RGB-to-RAW Image Reconstruction with Deterministic Latent Flow Matching
Zhen Liu, Diedong Feng, Hai Jiang +6
RGB-to-RAW reconstruction, or the reverse modeling of a camera Image Signal Processing (ISP) pipeline, aims to recover high-fidelity RAW data from RGB images. Despite notable progr…
Learning Efficient Meshflow and Optical Flow from Event Cameras
Xinglong Luo, Ao Luo, Kunming Luo +4
In this paper, we explore the problem of event-based meshflow estimation, a novel task that involves predicting a spatially smooth sparse motion field from event cameras. To start,…
Estimating 2D Camera Motion with Hybrid Motion Basis
Haipeng Li, Tianhao Zhou, Zhanglei Yang +6
Estimating 2D camera motion is a fundamental computer vision task that models the projection of 3D camera movements onto the 2D image plane. Current methods rely on either homograp…
Coding-Prior Guided Diffusion Network for Video Deblurring
Yike Liu, Jianhui Zhang, Haipeng Li +2
While recent video deblurring methods have advanced significantly, they often overlook two valuable prior information: (1) motion vectors (MVs) and coding residuals (CRs) from vide…
CodingHomo: Bootstrapping Deep Homography With Video Coding
Yike Liu, Haipeng Li, Shuaicheng Liu +1
Homography estimation is a fundamental task in computer vision with applications in diverse fields. Recent advances in deep learning have improved homography estimation, particular…
FlowPolicy: Enabling Fast and Robust 3D Flow-based Policy via Consistency Flow Matching for Robot Manipulation
Qinglun Zhang, Zhen Liu, Haoqiang Fan +3
Robots can acquire complex manipulation skills by learning policies from expert demonstrations, which is often known as vision-based imitation learning. Generating policies based o…
Ada-adapter:Fast Few-shot Style Personlization of Diffusion Model with Pre-trained Image Encoder
Jia Liu, Changlin Li, Qirui Sun +5
Fine-tuning advanced diffusion models for high-quality image stylization usually requires large training datasets and substantial computational resources, hindering their practical…
RecDiffusion: Rectangling for Image Stitching with Diffusion Models
Tianhao Zhou, Haipeng Li, Ziyi Wang +5
Image stitching from different captures often results in non-rectangular boundaries, which is often considered unappealing. To solve non-rectangular boundaries, current solutions i…
SpectralNeRF: Physically Based Spectral Rendering with Neural Radiance Field
Ru Li, Jia Liu, Guanghui Liu +3
In this paper, we propose SpectralNeRF, an end-to-end Neural Radiance Field (NeRF)-based architecture for high-quality physically based rendering from a novel spectral perspective.…