LightSwitch: Multi-view Relighting with Material-guided Diffusion
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.
68995f65f022d141f5d4353b
Yehonathan Litman
claimed_verified
2025-08-11T06:45:39.834Z
646ff038799a974be31bb344
/avatars/d9dc17246fba8360e709235f55445ef5.svg
Yehonathan Litman
user
thebluser
68995f65f022d141f5d4353c
Fernando De la Torre
68995f65f022d141f5d4353d
Shubham Tulsiani
LightSwitch: Multi-view Relighting with Material-guided Diffusion
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.
Lightswitch, a material-relighting diffusion framework, enhances 3D relighting by integrating multi-view and material cues, achieving superior quality and efficiency compared to existing methods.
material-relighting diffusion framework denoising scheme multi-view data intrinsic properties 2D relighting prediction diffusion inverse rendering