Generalizable Novel View Synthesis
5 papers with code • 1 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Generalizable Novel View Synthesis
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Most implemented papers
pixelNeRF: Neural Radiance Fields from One or Few Images
This allows the network to be trained across multiple scenes to learn a scene prior, enabling it to perform novel view synthesis in a feed-forward manner from a sparse set of views (as few as one).
Stereo Magnification with Multi-Layer Images
The second stage infers the color and the transparency values for these layers producing the final representation for novel view synthesis.
Neural Human Performer: Learning Generalizable Radiance Fields for Human Performance Rendering
YoungJoongUNC/Neural_Human_Performer
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NeurIPS 2021
To tackle this, we propose Neural Human Performer, a novel approach that learns generalizable neural radiance fields based on a parametric human body model for robust performance capture.
KeypointNeRF: Generalizing Image-based Volumetric Avatars using Relative Spatial Encoding of Keypoints
facebookresearch/KeypointNeRF
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In this work, we investigate common issues with existing spatial encodings and propose a simple yet highly effective approach to modeling high-fidelity volumetric humans from sparse views.
Self-improving Multiplane-to-layer Images for Novel View Synthesis
SamsungLabs/MLI
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We present a new method for lightweight novel-view synthesis that generalizes to an arbitrary forward-facing scene.
