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Related papers: All-frequency Full-body Human Image Relighting

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Previous face inverse rendering methods often require synthetic data with ground truth and/or professional equipment like a lighting stage. However, a model trained on synthetic data or using pre-defined lighting priors is typically unable…

Computer Vision and Pattern Recognition · Computer Science 2023-01-31 Meng Wang , Xiaojie Guo , Wenjing Dai , Jiawan Zhang

Light field imaging has recently known a regain of interest due to the availability of practical light field capturing systems that offer a wide range of applications in the field of computer vision. However, capturing high-resolution light…

Computer Vision and Pattern Recognition · Computer Science 2018-01-16 Reuben A. Farrugia , Christine Guillemot

We present Neural Microfacet Fields, a method for recovering materials, geometry, and environment illumination from images of a scene. Our method uses a microfacet reflectance model within a volumetric setting by treating each sample along…

Computer Vision and Pattern Recognition · Computer Science 2023-10-18 Alexander Mai , Dor Verbin , Falko Kuester , Sara Fridovich-Keil

We present a scheme for fast environment light estimation from the RGBD appearance of individual objects and their local image areas. Conventional inverse rendering is too computationally demanding for real-time applications, and the…

Computer Vision and Pattern Recognition · Computer Science 2020-08-07 Xin Wei , Guojun Chen , Yue Dong , Stephen Lin , Xin Tong

We present a method for harmonizing the lighting of a foreground video to match a target background scene, adjusting shadows, color tone, and illumination intensity (relightful harmonization). Unlike images, acquiring labeled data for…

Computer Vision and Pattern Recognition · Computer Science 2026-05-28 Jun Myeong Choi , Jae Shin Yoon , Luchao Qi , Roni Sengupta , Joon-Young Lee

Decomposing a scene into its shape, reflectance, and illumination is a challenging but important problem in computer vision and graphics. This problem is inherently more challenging when the illumination is not a single light source under…

Computer Vision and Pattern Recognition · Computer Science 2021-08-27 Mark Boss , Raphael Braun , Varun Jampani , Jonathan T. Barron , Ce Liu , Hendrik P. A. Lensch

Reconstructing the geometry and appearance of objects from photographs taken in different environments is difficult as the illumination and therefore the object appearance vary across captured images. This is particularly challenging for…

Computer Vision and Pattern Recognition · Computer Science 2024-12-20 Hadi Alzayer , Philipp Henzler , Jonathan T. Barron , Jia-Bin Huang , Pratul P. Srinivasan , Dor Verbin

Video portraits relighting is critical in user-facing human photography, especially for immersive VR/AR experience. Recent advances still fail to recover consistent relit result under dynamic illuminations from monocular RGB stream,…

Computer Vision and Pattern Recognition · Computer Science 2021-04-02 Longwen Zhang , Qixuan Zhang , Minye Wu , Jingyi Yu , Lan Xu

Relighting, which synthesizes a novel view under a given lighting condition (unseen in training time), is a must feature for immersive photo-realistic experience. However, real-time relighting is challenging due to high computation cost of…

Computer Vision and Pattern Recognition · Computer Science 2024-09-17 Euntae Choi , Vincent Carpentier , Seunghun Shin , Sungjoo Yoo

By supervising camera rays between a scene and multi-view image planes, NeRF reconstructs a neural scene representation for the task of novel view synthesis. On the other hand, shadow rays between the light source and the scene have yet to…

Computer Vision and Pattern Recognition · Computer Science 2023-03-24 Jingwang Ling , Zhibo Wang , Feng Xu

We develop a method that recovers the surface, materials, and illumination of a scene from its posed multi-view images. In contrast to prior work, it does not require any additional data and can handle glossy objects or bright lighting. It…

Computer Vision and Pattern Recognition · Computer Science 2025-04-08 Yue Fan , Ningjing Fan , Ivan Skorokhodov , Oleg Voynov , Savva Ignatyev , Evgeny Burnaev , Peter Wonka , Yiqun Wang

The reconstruction of indoor scenes from multi-view RGB images is challenging due to the coexistence of flat and texture-less regions alongside delicate and fine-grained regions. Recent methods leverage neural radiance fields aided by…

Computer Vision and Pattern Recognition · Computer Science 2024-08-14 Sheng Ye , Yubin Hu , Matthieu Lin , Yu-Hui Wen , Wang Zhao , Yong-Jin Liu , Wenping Wang

Image relighting has emerged as a problem of significant research interest inspired by augmented reality applications. Physics-based traditional methods, as well as black box deep learning models, have been developed. The existing deep…

Computer Vision and Pattern Recognition · Computer Science 2021-05-06 Amirsaeed Yazdani , Tiantong Guo , Vishal Monga

A fundamental problem in computer vision is that of inferring the intrinsic, 3D structure of the world from flat, 2D images of that world. Traditional methods for recovering scene properties such as shape, reflectance, or illumination rely…

Computer Vision and Pattern Recognition · Computer Science 2020-10-09 Jonathan T. Barron , Jitendra Malik

In this work, we use multi-view aerial images to reconstruct the geometry, lighting, and material of facades using neural signed distance fields (SDFs). Without the requirement of complex equipment, our method only takes simple RGB images…

Computer Vision and Pattern Recognition · Computer Science 2024-04-09 Zixuan Xie , Rengan Xie , Rong Li , Kai Huang , Pengju Qiao , Jingsen Zhu , Xu Yin , Qi Ye , Wei Hua , Yuchi Huo , Hujun Bao

Deep learning-based methods have made impressive progress in enhancing extremely low-light images - the image quality of the reconstructed images has generally improved. However, we found out that most of these methods could not…

Image and Video Processing · Electrical Eng. & Systems 2022-04-05 Pohao Hsu , Che-Tsung Lin , Chun Chet Ng , Jie-Long Kew , Mei Yih Tan , Shang-Hong Lai , Chee Seng Chan , Christopher Zach

Every time you sit in front of a TV or monitor, your face is actively illuminated by time-varying patterns of light. This paper proposes to use this time-varying illumination for synthetic relighting of your face with any new illumination…

Computer Vision and Pattern Recognition · Computer Science 2021-11-12 Soumyadip Sengupta , Brian Curless , Ira Kemelmacher-Shlizerman , Steve Seitz

The light transport (LT) of a scene describes how it appears under different lighting and viewing directions, and complete knowledge of a scene's LT enables the synthesis of novel views under arbitrary lighting. In this paper, we focus on…

Human relighting is a highly desirable yet challenging task. Existing works either require expensive one-light-at-a-time (OLAT) captured data using light stage or cannot freely change the viewpoints of the rendered body. In this work, we…

Computer Vision and Pattern Recognition · Computer Science 2022-09-21 Zhaoxi Chen , Ziwei Liu

All existing image enhancement methods, such as HDR tone mapping, cannot recover A/D quantization losses due to insufficient or excessive lighting, (underflow and overflow problems). The loss of image details due to A/D quantization is…

Computer Vision and Pattern Recognition · Computer Science 2018-03-22 Chang Liu , Xiaolin Wu , Xiao Shu
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