SViM3D: Stable Video Material Diffusion for Single Image 3D Generation
Authors: name: Andreas Engelhardt, name: Mark Boss, name: Vikram Voletti, name: Chun-Han Yao, name: Hendrik P. A. Lensch, name: Varun Jampani
Organization: Stability AI

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 objects from a single image efficiently. However, reflectance is
still represented by simple material models or needs to be estimated in
additional steps to enable relighting and controlled appearance edits. We
extend a latent video diffusion model to output spatially varying PBR
parameters and surface normals jointly with each generated view based on
explicit camera control. This unique setup allows for relighting and generating
a 3D asset using our model as neural prior. We introduce various mechanisms to
this pipeline that improve quality in this ill-posed setting. We show
state-of-the-art relighting and novel view synthesis performance on multiple
object-centric datasets. Our method generalizes to diverse inputs, enabling the
generation of relightable 3D assets useful in AR/VR, movies, games and other
visual media.