The past two years have witnessed a significant increase in interest concerning NeRF-based human body rendering. While this surge has propelled considerable advancements, it has also led to an influx of methods and datasets. This explosion complicates experimental settings and makes fair comparisons challenging. In this work, we design and execute thorough studies into unified evaluation settings and metrics to establish a fair and reasonable benchmark for human NeRF models. To reveal the effects of extant models, we benchmark them against diverse and hard scenes. Additionally, we construct a cross-subject benchmark pre-trained on large-scale datasets to assess generalizable methods. Finally, we analyze the essential components for animatability and generalizability, and make HumanNeRF from monocular videos generalizable, as the inaugural baseline. We hope these benchmarks and analyses could serve the community.

The past two years have witnessed a significant increase in interest concerning NeRF-based human body rendering. While this surge has propelled considerable advancements, it has also led to an influx of methods and datasets. This explosion complicates experimental settings and makes fair comparisons challenging. In this work, we design and execute thorough studies into unified evaluation settings and metrics to establish a fair and reasonable benchmark for human NeRF models. To reveal the effects of extant models, we benchmark them against diverse and hard scenes.

In this post I survey a collection of Computer Vision Models that have made key advancements for View Synthesis. The fundamental idea behind View synthesis is the ability to take two-dimensional images, or videos, from different camera viewpoints and construct realistic novel views from them. Being able to synthesize a realistic novel view can depend on many factors such as, sufficient input images across various viewpoints and quality or resolution of the provided images. I will be only discussing models that have produced satisfactory results given their set of input and test images. Specifically, I have researched SRN (Scene Representation Networks), NeRF (Neural Radiance Fields), and NeuMan (Neural Human Radiance Field From a Single Video).