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This paper introduces Comprehensive Relighting, the first all-in-one approach that can both control and harmonize the lighting from an image or video of humans with arbitrary body parts from any scene. Building such a generalizable model is…

Computer Vision and Pattern Recognition · Computer Science 2025-04-07 Junying Wang , Jingyuan Liu , Xin Sun , Krishna Kumar Singh , Zhixin Shu , He Zhang , Jimei Yang , Nanxuan Zhao , Tuanfeng Y. Wang , Simon S. Chen , Ulrich Neumann , Jae Shin Yoon

The advancement of generative radiance fields has pushed the boundary of 3D-aware image synthesis. Motivated by the observation that a 3D object should look realistic from multiple viewpoints, these methods introduce a multi-view constraint…

Computer Vision and Pattern Recognition · Computer Science 2021-12-10 Xingang Pan , Xudong Xu , Chen Change Loy , Christian Theobalt , Bo Dai

Inverse rendering is the problem of decomposing an image into its intrinsic components, i.e. albedo, normal and lighting. To solve this ill-posed problem from single image, state-of-the-art methods in shape from shading mostly resort to…

Computer Vision and Pattern Recognition · Computer Science 2021-06-30 Mona Zehni , Shaona Ghosh , Krishna Sridhar , Sethu Raman

Understanding and modeling lighting effects are fundamental tasks in computer vision and graphics. Classic physically-based rendering (PBR) accurately simulates the light transport, but relies on precise scene representations--explicit 3D…

Computer Vision and Pattern Recognition · Computer Science 2025-03-25 Ruofan Liang , Zan Gojcic , Huan Ling , Jacob Munkberg , Jon Hasselgren , Zhi-Hao Lin , Jun Gao , Alexander Keller , Nandita Vijaykumar , Sanja Fidler , Zian Wang

We present Stable Video Materials 3D (SViM3D), a framework to predict multi-view consistent physically based rendering (PBR) materials, given a single image. Recently, video diffusion models have been successfully used to reconstruct 3D…

Graphics · Computer Science 2025-11-04 Andreas Engelhardt , Mark Boss , Vikram Voleti , Chun-Han Yao , Hendrik P. A. Lensch , Varun Jampani

Single-image relighting is highly under-constrained: small illumination changes can produce large, nonlinear variations in shading, shadows, and specularities, while geometry and materials remain unobserved. Existing diffusion-based…

Computer Vision and Pattern Recognition · Computer Science 2026-03-17 Haoze Zheng , Zihao Wang , Xianfeng Wu , Yajing Bai , Yexin Liu , Yun Li , Xiaogang Xu , Harry Yang

Outdoor scene relighting is a challenging problem that requires good understanding of the scene geometry, illumination and albedo. Current techniques are completely supervised, requiring high quality synthetic renderings to train a…

Computer Vision and Pattern Recognition · Computer Science 2021-07-08 Ye Yu , Abhimitra Meka , Mohamed Elgharib , Hans-Peter Seidel , Christian Theobalt , William A. P. Smith

Recent advancements in image relighting models, driven by large-scale datasets and pre-trained diffusion models, have enabled the imposition of consistent lighting. However, video relighting still lags, primarily due to the excessive…

Computer Vision and Pattern Recognition · Computer Science 2025-03-13 Yujie Zhou , Jiazi Bu , Pengyang Ling , Pan Zhang , Tong Wu , Qidong Huang , Jinsong Li , Xiaoyi Dong , Yuhang Zang , Yuhang Cao , Anyi Rao , Jiaqi Wang , Li Niu

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…

We propose an automatic method to infer high dynamic range illumination from a single, limited field-of-view, low dynamic range photograph of an indoor scene. In contrast to previous work that relies on specialized image capture, user…

Computer Vision and Pattern Recognition · Computer Science 2017-11-22 Marc-André Gardner , Kalyan Sunkavalli , Ersin Yumer , Xiaohui Shen , Emiliano Gambaretto , Christian Gagné , Jean-François Lalonde

Given a monocular video, the goal of video re-rendering is to generate views of the scene from a novel camera trajectory. Existing methods face two distinct challenges. Geometrically unconditioned models lack spatial awareness, leading to…

Computer Vision and Pattern Recognition · Computer Science 2026-04-03 Mingyang Xie , Numair Khan , Tianfu Wang , Naina Dhingra , Seonghyeon Nam , Haitao Yang , Zhuo Hui , Christopher Metzler , Andrea Vedaldi , Hamed Pirsiavash , Lei Luo

We introduce ROGR, a novel approach that reconstructs a relightable 3D model of an object captured from multiple views, driven by a generative relighting model that simulates the effects of placing the object under novel environment…

Computer Vision and Pattern Recognition · Computer Science 2025-12-05 Jiapeng Tang , Matthew Levine , Dor Verbin , Stephan J. Garbin , Matthias Nießner , Ricardo Martin Brualla , Pratul P. Srinivasan , Philipp Henzler

Recent generative methods for single-shot high dynamic range (HDR) image reconstruction show promising results, but often struggle with preserving fidelity to the input image. They require separate models to handle highlights and shadows,…

Computer Vision and Pattern Recognition · Computer Science 2026-05-13 Chinmay Talegaonkar , Jinshi He , Christopher McKenna , Nicholas Antipa

Authoring high-quality digital materials is key to realism in 3D rendering. Previous generative models for materials have been trained exclusively on synthetic data; such data is limited in availability and has a visual gap to real…

Computer Vision and Pattern Recognition · Computer Science 2023-05-24 Xilong Zhou , Miloš Hašan , Valentin Deschaintre , Paul Guerrero , Yannick Hold-Geoffroy , Kalyan Sunkavalli , Nima Khademi Kalantari

In this paper, we present EdgeRelight360, an approach for real-time video portrait relighting on mobile devices, utilizing text-conditioned generation of 360-degree high dynamic range image (HDRI) maps. Our method proposes a diffusion-based…

Computer Vision and Pattern Recognition · Computer Science 2024-04-16 Min-Hui Lin , Mahesh Reddy , Guillaume Berger , Michel Sarkis , Fatih Porikli , Ning Bi

Relighting is an essential step in realistically transferring objects from a captured image into another environment. For example, authentic telepresence in Augmented Reality requires faces to be displayed and relit consistent with the…

Computer Vision and Pattern Recognition · Computer Science 2020-04-21 Thomas Nestmeyer , Jean-François Lalonde , Iain Matthews , Andreas M. Lehrmann

Reconstructing a renderable 3D model from images is a useful but challenging task. Recent feedforward 3D reconstruction methods have demonstrated remarkable success in efficiently recovering geometry, but still cannot accurately model the…

Computer Vision and Pattern Recognition · Computer Science 2026-03-25 Zekai Gu , Shuoxuan Feng , Yansong Wang , Hanzhuo Huang , Zhongshuo Du , Chengfeng Zhao , Chengwei Ren , Peng Wang , Yuan Liu

Generating 3D scenes is a challenging open problem, which requires synthesizing plausible content that is fully consistent in 3D space. While recent methods such as neural radiance fields excel at view synthesis and 3D reconstruction, they…

Computer Vision and Pattern Recognition · Computer Science 2024-02-22 Titas Anciukevičius , Fabian Manhardt , Federico Tombari , Paul Henderson

We present a method that takes as input a set of images of a scene illuminated by unconstrained known lighting, and produces as output a 3D representation that can be rendered from novel viewpoints under arbitrary lighting conditions. Our…

Computer Vision and Pattern Recognition · Computer Science 2020-12-08 Pratul P. Srinivasan , Boyang Deng , Xiuming Zhang , Matthew Tancik , Ben Mildenhall , Jonathan T. Barron

We propose a generative model that, given a coarsely edited image, synthesizes a photorealistic output that follows the prescribed layout. Our method transfers fine details from the original image and preserve the identity of its parts.…

Computer Vision and Pattern Recognition · Computer Science 2025-08-08 Hadi Alzayer , Zhihao Xia , Xuaner Zhang , Eli Shechtman , Jia-Bin Huang , Michael Gharbi
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