PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization

TL;DR

PIFu is a novel deep learning approach that accurately reconstructs high-resolution 3D models of clothed humans from single or multiple images, capturing intricate details and variations.

Contribution

It introduces Pixel-aligned Implicit Function (PIFu), a memory-efficient, high-resolution 3D reconstruction method that handles arbitrary topology and extends to multiple views.

Findings

Achieves state-of-the-art performance on a public benchmark.

Successfully reconstructs detailed clothing and hairstyles.

Handles arbitrary number of input views.

Abstract

We introduce Pixel-aligned Implicit Function (PIFu), a highly effective implicit representation that locally aligns pixels of 2D images with the global context of their corresponding 3D object. Using PIFu, we propose an end-to-end deep learning method for digitizing highly detailed clothed humans that can infer both 3D surface and texture from a single image, and optionally, multiple input images. Highly intricate shapes, such as hairstyles, clothing, as well as their variations and deformations can be digitized in a unified way. Compared to existing representations used for 3D deep learning, PIFu can produce high-resolution surfaces including largely unseen regions such as the back of a person. In particular, it is memory efficient unlike the voxel representation, can handle arbitrary topology, and the resulting surface is spatially aligned with the input image. Furthermore, while previous techniques are designed to process either a single image or multiple views, PIFu extends naturally to arbitrary number of views. We demonstrate high-resolution and robust reconstructions on real world images from the DeepFashion dataset, which contains a variety of challenging clothing types. Our method achieves state-of-the-art performance on a public benchmark and outperforms the prior work for clothed human digitization from a single image.