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Found 9 papers, 3 papers with code
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.
Learning to Relight Portrait Images via a Virtual Light Stage and Synthetic-to-Real Adaptation
An effective approach is to supervise the training of deep neural networks with a high-fidelity dataset of desired input-output pairs, captured with a light stage.
PhotoScene: Photorealistic Material and Lighting Transfer for Indoor Scenes
Most indoor 3D scene reconstruction methods focus on recovering 3D geometry and scene layout.
OpenRooms: An Open Framework for Photorealistic Indoor Scene Datasets
Finally, we demonstrate that our framework may also be integrated with physics engines, to create virtual robotics environments with unique ground truth such as friction coefficients and correspondence to real scenes.
OpenRooms: An End-to-End Open Framework for Photorealistic Indoor Scene Datasets
Finally, we demonstrate that our framework may also be integrated with physics engines, to create virtual robotics environments with unique ground truth such as friction coefficients and correspondence to real scenes.
Through the Looking Glass: Neural 3D Reconstruction of Transparent Shapes
Recovering the 3D shape of transparent objects using a small number of unconstrained natural images is an ill-posed problem.
A Unified Feature Disentangler for Multi-Domain Image Translation and Manipulation
We present a novel and unified deep learning framework which is capable of learning domain-invariant representation from data across multiple domains.
Adaptation and Re-Identification Network: An Unsupervised Deep Transfer Learning Approach to Person Re-Identification
Person re-identification (Re-ID) aims at recognizing the same person from images taken across different cameras.
Ranked #19 on
Unsupervised Domain Adaptation
on Duke to Market
Detach and Adapt: Learning Cross-Domain Disentangled Deep Representation
While representation learning aims to derive interpretable features for describing visual data, representation disentanglement further results in such features so that particular image attributes can be identified and manipulated.
