Novel View Synthesis
Synthesize a target image with an arbitrary target camera pose from given source images and their camera poses.
State-of-the-art leaderboards
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Greatest papers with code
Deferred Neural Rendering: Image Synthesis using Neural Textures
Similar to traditional textures, neural textures are stored as maps on top of 3D mesh proxies; however, the high-dimensional feature maps contain significantly more information, which can be interpreted by our new deferred neural rendering pipeline.
Local Light Field Fusion: Practical View Synthesis with Prescriptive Sampling Guidelines
• Fyusion/LLFF
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We present a practical and robust deep learning solution for capturing and rendering novel views of complex real world scenes for virtual exploration.
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.
Transformable Bottleneck Networks
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).
Monocular Neural Image Based Rendering with Continuous View Control
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.
HoloGAN: Unsupervised learning of 3D representations from natural images
• christopher-beckham/hologan-pytorch
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This shows that HoloGAN is the first generative model that learns 3D representations from natural images in an entirely unsupervised manner.