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Found 11 papers, 3 papers with code
DATENeRF: Depth-Aware Text-based Editing of NeRFs
However, extending these techniques to edit scenes in Neural Radiance Fields (NeRF) is complex, as editing individual 2D frames can result in inconsistencies across multiple views.
LightIt: Illumination Modeling and Control for Diffusion Models
Our method is the first that enables the generation of images with controllable, consistent lighting and performs on par with specialized relighting state-of-the-art methods.
Materialistic: Selecting Similar Materials in Images
We present a method capable of selecting the regions of a photograph exhibiting the same material as an artist-chosen area.
JoIN: Joint GANs Inversion for Intrinsic Image Decomposition
In this work, we propose to solve ill-posed inverse imaging problems using a bank of Generative Adversarial Networks (GAN) as a prior and apply our method to the case of Intrinsic Image Decomposition for faces and materials.
Floaters No More: Radiance Field Gradient Scaling for Improved Near-Camera Training
NeRF acquisition typically requires careful choice of near planes for the different cameras or suffers from background collapse, creating floating artifacts on the edges of the captured scene.
PixHt-Lab: Pixel Height Based Light Effect Generation for Image Compositing
To compensate for the lack of geometry in 2D Image compositing, recent deep learning-based approaches introduced a pixel height representation to generate soft shadows and reflections.
OutCast: Outdoor Single-image Relighting with Cast Shadows
We propose a relighting method for outdoor images.
Point-NeRF: Point-based Neural Radiance Fields
Point-NeRF combines the advantages of these two approaches by using neural 3D point clouds, with associated neural features, to model a radiance field.
Point-Based Neural Rendering with Per-View Optimization
There has recently been great interest in neural rendering methods.
Free-viewpoint Indoor Neural Relighting from Multi-view Stereo
We design a convolutional network around input feature maps that facilitate learning of an implicit representation of scene materials and illumination, enabling both relighting and free-viewpoint navigation.
Deep Blending for Free-Viewpoint Image-Based-Rendering
We present a new deep learning approach to blending for IBR, in which we use held-out real image data to learn blending weights to combine input photo contributions.
