Generalizable Novel View Synthesis

5 papers with code • 1 benchmarks • 2 datasets

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Most implemented papers

pixelNeRF: Neural Radiance Fields from One or Few Images

sxyu/pixel-nerf CVPR 2021

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

SamsungLabs/MLI CVPR 2022

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 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 10 May 2022

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 4 Oct 2022

We present a new method for lightweight novel-view synthesis that generalizes to an arbitrary forward-facing scene.