Detailed Human Avatars from Monocular Video

Abstract

We present a novel method for high detail-preserving human avatar creation from monocular video. A parameterized body model is refined and optimized to maximally resemble subjects from a video showing them from all sides. Our avatars feature a natural face, hairstyle, clothes with garment wrinkles, and high-resolution texture. Our paper contributes facial landmark and shading-based human body shape refinement, a semantic texture prior, and a novel texture stitching strategy, resulting in the most sophisticated-looking human avatars obtained from a single video to date. Numerous results show the robustness and versatility of our method. A user study illustrates its superiority over the state-of-the-art in terms of identity preservation, level of detail, realism, and overall user preference.

arXiv commentInternational Conference on 3D Vision (3DV) 2018

References in corpus (2)

• Look into Person: Self-supervised Structure-sensitive Learning and A New Benchmark for Human Parsing
• Video Based Reconstruction of 3D People Models

Cited by in corpus (3)

• RGB-D-based Framework to Acquire, Visualize and Measure the Human Body for Dietetic Treatments
• Recovering Geometric Information with Learned Texture Perturbations
• 3D Human Texture Estimation from a Single Image with Transformers