We present a method for fast 3D reconstruction and real-time rendering of dynamic humans from monocular videos with accompanying parametric body fits. Our method can reconstruct a dynamic human in less than 3h using a single GPU, compared to recent state-of-the-art alternatives that take up to 72h.
These speedups are obtained by using a lightweight deformation model solely based on linear blend skinning, and an efficient factorized volumetric representation for modeling the shape and color of the person in canonical pose. Moreover, we propose a novel local ray marching rendering which, by exploiting standard GPU hardware and without any baking or conversion of the radiance field, allows visualizing the neural human on a mobile VR device at 40 frames per second with minimal loss of visual quality.
Our experimental evaluation shows superior or competitive results with state-of-the art methods while obtaining large training speedup, using a simple model, and achieving real-time rendering.
These videos were recorded from Virtual / Mixed Reality headset with color passthrough capabilities. We render reconstructed humans from ZJU-MoCap dataset with our custom shaders using TensoRF representation. Animation is done with linear blend skinning.
We also ported our code to WebGL to show real-time rendering capabilities and robustness of our rendering approach: it can run across most popular platforms.
@article{,
author = {Rocco, Ignacio and Makarov, Iurii and Kokkinos, Filippos and Novotny, David and Graham, Benjamin and Neverova, Natalia and Vedaldi, Andrea},
title = {Real-time volumetric rendering of dynamic humans},
journal = {arXiv},
year = {2023},
}