RESEARCH

COMPUTER VISION

ARCH: Animatable Reconstruction of Clothed Humans

June 15, 2020

Abstract

In this paper, we propose ARCH (Animatable Reconstruction of Clothed Humans), a novel end-to-end framework for accurate reconstruction of animation-ready 3D clothed humans from a monocular image. Existing approaches to digitize 3D humans struggle to handle pose variations and recover details. Also, they do not produce models that are animation ready. In contrast, ARCH is a learned pose-aware model that produces detailed 3D rigged full-body human avatars from a single unconstrained RGB image. A Semantic Space and a Semantic Deformation Field are created using a parametric 3D body estimator. They allow the transformation of 2D/3D clothed humans into a canonical space, reducing ambiguities in geometry caused by pose variations and occlusions in training data. Detailed surface geometry and appearance are learned using an implicit function representation with spatial local features. Furthermore, we propose additional per-pixel supervision on the 3D reconstruction using opacity-aware differentiable rendering. Our experiments indicate that ARCH increases the fidelity of the reconstructed humans. We obtain more than 50% lower reconstruction errors for standard metrics compared to state-of-the-art methods on public datasets. We also show numerous qualitative examples of animated, high-quality reconstructed avatars unseen in the literature so far.

Download the Paper

AUTHORS

Written by

Zeng Huang

Yuanlu Xu

Christoph Lassner

Hao Li

Tony Tung

Publisher

Conference on Computer Vision and Pattern Recognition (CVPR)

Research Topics

Computer Vision

Related Publications

June 15, 2019

COMPUTER VISION

FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search | Facebook AI Research

Designing accurate and efficient ConvNets for mobile devices is challenging because the design space is combinatorially large. Due to this, previous neural architecture search (NAS) methods are computationally expensive. ConvNet architecture…

Bichen Wu, Xiaoliang Dai, Peizhao Zhang, Yanghan Wang, Fei Sun, Yiming Wu, Yuandong Tian, Peter Vajda, Yangqing Jia, Kurt Keutzer

June 15, 2019

April 28, 2019

COMPUTER VISION

Inverse Path Tracing for Joint Material and Lighting Estimation | Facebook AI Research

Modern computer vision algorithms have brought significant advancement to 3D geometry reconstruction. However, illumination and material reconstruction remain less studied, with current approaches assuming very simplified models for materials…

Dejan Azinović, Tzu-Mao Li, Anton Kaplanyan, Matthias Nießner

April 28, 2019

June 14, 2019

COMPUTER VISION

Thinking Outside the Pool: Active Training Image Creation for Relative Attributes | Facebook AI Research

Current wisdom suggests more labeled image data is always better, and obtaining labels is the bottleneck. Yet curating a pool of sufficiently diverse and informative images is itself a challenge. In particular, training image curation is…

Aron Yu, Kristen Grauman

June 14, 2019

September 09, 2018

COMPUTER VISION

DDRNet: Depth Map Denoising and Refinement for Consumer Depth Cameras Using Cascaded CNNs | Facebook AI Research

Consumer depth sensors are more and more popular and come to our daily lives marked by its recent integration in the latest iPhone X. However, they still suffer from heavy noises which dramatically limit their applications. Although plenty of…

Shi Yan, Chenglei Wu, Lizhen Wang, Feng Xu, Liang An, Kaiwen Guo, Yebin Liu

September 09, 2018

Help Us Pioneer The Future of AI

We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.