Philosophy We strive to create an environment conducive to many different types of research across many different time scales and levels of risk. Learn more about our Philosophy Learn more
タグ検索の該当結果が少ないため、タイトル検索結果を表示しています。
Philosophy We strive to create an environment conducive to many different types of research across many different time scales and levels of risk. Learn more about our Philosophy Learn more
Given a single image, we reconstruct the full 3D geometry – including self-occluded (or unseen) regions – of the photographed person, together with albedo and shaded surface color. Our end-to-end trainable pipeline requires no image matting and reconstructs all outputs in a single step. We present PHORHUM, a novel, end-to-end trainable, deep neural network methodology for photorealistic 3D human r
We propose a method that can generate highly detailed high-resolution depth estimations from a single image. Our method is based on optimizing the performance of a pre-trained network by merging estimations in different resolutions and different patches to generate a high-resolution estimate. Abstract Neural networks have shown great abilities in estimating depth from a single image. However, the
Abstract We introduce a free-viewpoint rendering method -- HumanNeRF -- that works on a given monocular video of a human performing complex body motions, e.g. a video from YouTube. Our method enables pausing the video at any frame and rendering the subject from arbitrary new camera viewpoints or even a full 360-degree camera path for that particular frame and body pose. This task is particularly c
j次のブックマーク
k前のブックマーク
lあとで読む
eコメント一覧を開く
oページを開く