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Found 8 papers, 4 papers with code
SV3D: Novel Multi-view Synthesis and 3D Generation from a Single Image using Latent Video Diffusion
In this work, we propose SV3D that adapts image-to-video diffusion model for novel multi-view synthesis and 3D generation, thereby leveraging the generalization and multi-view consistency of the video models, while further adding explicit camera control for NVS.
Collaborative Control for Geometry-Conditioned PBR Image Generation
Current 3D content generation approaches build on diffusion models that output RGB images.
SHINOBI: Shape and Illumination using Neural Object Decomposition via BRDF Optimization In-the-wild
We present SHINOBI, an end-to-end framework for the reconstruction of shape, material, and illumination from object images captured with varying lighting, pose, and background.
SAMURAI: Shape And Material from Unconstrained Real-world Arbitrary Image collections
Our method works on in-the-wild online image collections of an object and produces relightable 3D assets for several use-cases such as AR/VR.
Neural-PIL: Neural Pre-Integrated Lighting for Reflectance Decomposition
Decomposing a scene into its shape, reflectance and illumination is a fundamental problem in computer vision and graphics.
NeRD: Neural Reflectance Decomposition from Image Collections
This problem is inherently more challenging when the illumination is not a single light source under laboratory conditions but is instead an unconstrained environmental illumination.
Ranked #5 on
Image Relighting
on Stanford-ORB
Two-shot Spatially-varying BRDF and Shape Estimation
Extensive experiments on both synthetic and real-world datasets show that our network trained on a synthetic dataset can generalize well to real-world images.
Single Image BRDF Parameter Estimation with a Conditional Adversarial Network
Creating plausible surfaces is an essential component in achieving a high degree of realism in rendering.
