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Found 10 papers, 3 papers with code
NeRF Analogies: Example-Based Visual Attribute Transfer for NeRFs
A Neural Radiance Field (NeRF) encodes the specific relation of 3D geometry and appearance of a scene.
ReplaceAnything3D:Text-Guided 3D Scene Editing with Compositional Neural Radiance Fields
We introduce ReplaceAnything3D model (RAM3D), a novel text-guided 3D scene editing method that enables the replacement of specific objects within a scene.
TextureDreamer: Image-guided Texture Synthesis through Geometry-aware Diffusion
In contrast, TextureDreamer can transfer highly detailed, intricate textures from real-world environments to arbitrary objects with only a few casually captured images, potentially significantly democratizing texture creation.
GauFRe: Gaussian Deformation Fields for Real-time Dynamic Novel View Synthesis
We propose a method for dynamic scene reconstruction using deformable 3D Gaussians that is tailored for monocular video.
AlteredAvatar: Stylizing Dynamic 3D Avatars with Fast Style Adaptation
Among existing approaches for avatar stylization, direct optimization methods can produce excellent results for arbitrary styles but they are unpleasantly slow.
SNeRF: Stylized Neural Implicit Representations for 3D Scenes
This paper presents a stylized novel view synthesis method.
Self-Supervised Object Detection via Generative Image Synthesis
We present SSOD, the first end-to-end analysis-by synthesis framework with controllable GANs for the task of self-supervised object detection.
BlockGAN: Learning 3D Object-aware Scene Representations from Unlabelled Images
Our experiments show that using explicit 3D features to represent objects allows BlockGAN to learn disentangled representations both in terms of objects (foreground and background) and their properties (pose and identity).
HoloGAN: Unsupervised learning of 3D representations from natural images
This shows that HoloGAN is the first generative model that learns 3D representations from natural images in an entirely unsupervised manner.
RenderNet: A deep convolutional network for differentiable rendering from 3D shapes
We present RenderNet, a differentiable rendering convolutional network with a novel projection unit that can render 2D images from 3D shapes.
