LightSwitch：基於材質引導擴散的多視角重光照技術

LightSwitch: Multi-view Relighting with Material-guided Diffusion

August 8, 2025

作者: Yehonathan Litman, Fernando De la Torre, Shubham Tulsiani

cs.AI

摘要

近期在3D重光照領域的研究顯示出將2D圖像重光照生成先驗整合以改變3D表現外觀，同時保留其基礎結構的潛力。然而，直接用於從輸入圖像直接重光照的2D重光照生成先驗，未能充分利用可推斷的主體內在屬性，也無法大規模考慮多視角數據，導致重光照效果欠佳。本文提出Lightswitch，一種新穎的微調材質重光照擴散框架，它能夠高效地將任意數量的輸入圖像重光照至目標光照條件，並融入推斷出的內在屬性線索。通過結合多視角與材質信息提示以及可擴展的去噪方案，我們的方法能夠一致且高效地對具有多樣材質構成的物體進行密集多視角數據重光照。我們證明，在2D重光照預測質量上，我們的方法超越了以往直接從圖像重光照的頂尖重光照先驗。此外，我們進一步展示，LightSwitch在重光照合成與真實物體時，僅需短短2分鐘，便能與或超越現有頂尖的擴散逆渲染方法。

English

Recent approaches for 3D relighting have shown promise in integrating 2D image relighting generative priors to alter the appearance of a 3D representation while preserving the underlying structure. Nevertheless, generative priors used for 2D relighting that directly relight from an input image do not take advantage of intrinsic properties of the subject that can be inferred or cannot consider multi-view data at scale, leading to subpar relighting. In this paper, we propose Lightswitch, a novel finetuned material-relighting diffusion framework that efficiently relights an arbitrary number of input images to a target lighting condition while incorporating cues from inferred intrinsic properties. By using multi-view and material information cues together with a scalable denoising scheme, our method consistently and efficiently relights dense multi-view data of objects with diverse material compositions. We show that our 2D relighting prediction quality exceeds previous state-of-the-art relighting priors that directly relight from images. We further demonstrate that LightSwitch matches or outperforms state-of-the-art diffusion inverse rendering methods in relighting synthetic and real objects in as little as 2 minutes.