Novel View Synthesis
Synthesize a target image with an arbitrary target camera pose from given source images and their camera poses.
( Image credit: Multi-view to Novel view: Synthesizing novel views with Self-Learned Confidence )
Leaderboards
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Greatest papers with code
Local Light Field Fusion: Practical View Synthesis with Prescriptive Sampling Guidelines
• Fyusion/LLFF
• 
We present a practical and robust deep learning solution for capturing and rendering novel views of complex real world scenes for virtual exploration.
Stereo Magnification: Learning View Synthesis using Multiplane Images
• google/stereo-magnification
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The view synthesis problem--generating novel views of a scene from known imagery--has garnered recent attention due in part to compelling applications in virtual and augmented reality.
HoloGAN: Unsupervised learning of 3D representations from natural images
ICCV 2019
• thunguyenphuoc/HoloGAN
•
This shows that HoloGAN is the first generative model that learns 3D representations from natural images in an entirely unsupervised manner.
Multi-view to Novel view: Synthesizing Novel Views with Self-Learned Confidence
Proceedings of the 15th European Conference on Computer Vision, 2018
• shaohua0116/Multiview2Novelview
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We address the task of multi-view novel view synthesis, where we are interested in synthesizing a target image with an arbitrary camera pose from given source images.
DeepVoxels: Learning Persistent 3D Feature Embeddings
CVPR 2019
• vsitzmann/deepvoxels
• 
In this work, we address the lack of 3D understanding of generative neural networks by introducing a persistent 3D feature embedding for view synthesis.
Transformable Bottleneck Networks
ICCV 2019
• kyleolsz/TB-Networks
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We propose a novel approach to performing fine-grained 3D manipulation of image content via a convolutional neural network, which we call the Transformable Bottleneck Network (TBN).
Deep Blending for Free-Viewpoint Image-Based-Rendering
SIGGRAPH Asia 2018 2018
• Phog/DeepBlending
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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.
Monocular Neural Image Based Rendering with Continuous View Control
ICCV 2019
• xuchen-ethz/continuous_view_synthesis
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The approach is self-supervised and only requires 2D images and associated view transforms for training.
Cross-Domain 3D Equivariant Image Embeddings
• machc/spherical_embeddings
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This embedding encodes images with 3D shape properties and is equivariant to 3D rotations of the observed object.