FACSIMILE: Fast and Accurate Scans From an Image in Less Than a Second

Smith, David L.; Loper, Margaret L.; Hu, Xiaochen; Mavroidis, Paris; Romero, Javier

doi:10.1109/iccv.2019.00543

Cited by 55 publications

(36 citation statements)

References 51 publications

(92 reference statements)

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“…While this method obtains impressive results, the cubic memory requirement imposed by the discrete voxel representation prevents obtaining high resolution simply by naively scaling the input resolution. Alternative methods consider additional free-form deformation on top of a parametric model space [1], and there exist also multiple approaches that predict depth maps of the target people as output [40,11,39].…”

Section: Related Workmentioning

confidence: 99%

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

Saito

Huang

Natsume

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

978

961

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Recent advances in image-based 3D human shape estimation have been driven by the significant improvement in representation power afforded by deep neural networks. Although current approaches have demonstrated the potential in real world settings, they still fail to produce reconstructions with the level of detail often present in the input images. We argue that this limitation stems primarily form two conflicting requirements; accurate predictions require large context, but precise predictions require high resolution. Due to memory limitations in current hardware, previous approaches tend to take low resolution images as input to cover large spatial context, and produce less precise (or low resolution) 3D estimates as a result. We address this limitation by formulating a multi-level architecture that is end-to-end trainable. A coarse level observes the whole image at lower resolution and focuses on holistic reasoning. This provides context to an fine level which estimates highly detailed geometry by observing higher-resolution images. We demonstrate that our approach significantly outperforms existing state-of-the-art techniques on single image human shape reconstruction by fully leveraging 1k-resolution input images.

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Section: Related Workmentioning

confidence: 99%

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

Saito

Huang

Natsume

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

978

961

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“…Reconstruction of 3D humans from 2D images and videos is a classical computer vision problem. Most approaches [9,17,23,26,27,30,36,38,46] output 3D body meshes from images, but not clothing. This ignores image evidence that may be useful.…”

Section: Related Workmentioning

confidence: 99%

Learning to Dress 3D People in Generative Clothing

Yang

Ranjan

et al. 2020

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

243

210

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Three-dimensional human body models are widely used in the analysis of human pose and motion. Existing models, however, are learned from minimally-clothed 3D scans and thus do not generalize to the complexity of dressed people in common images and videos. Additionally, current models lack the expressive power needed to represent the complex non-linear geometry of pose-dependent clothing shapes. To address this, we learn a generative 3D mesh model of clothed people from 3D scans with varying pose and clothing. Specifically, we train a conditional Mesh-VAE-GAN to learn the clothing deformation from the SMPL body model, making clothing an additional term in SMPL. Our model is conditioned on both pose and clothing type, giving the ability to draw samples of clothing to dress different body shapes in a variety of styles and poses. To preserve wrinkle detail, our Mesh-VAE-GAN extends patchwise discriminators to 3D meshes. Our model, named CAPE, represents global shape and fine local structure, effectively extending the SMPL body model to clothing. To our knowl-edge, this is the first generative model that directly dresses 3D human body meshes and generalizes to different poses. The model, code and data are available for research purposes at https://cape.is.tue.mpg.de.

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“…Single-view performance capture Many methods have been proposed to reconstruct detailed geometry from single-view inputs. Typical techniques include silhouette estimation [30], depth estimation [12,35] and templatebased deformation [1,49,16]. Moreover, SMPL [28] regression or optimization can be incorporated to generate more reliable and robust outputs as shown in [48,47].…”

Section: Related Workmentioning

confidence: 99%