ClothCap

By Gerard Pons-Moll, Sergi Pujades, Sonny Hu, Michael J. Black. ACM Transactions on Graphics, 2017

379 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/c0de99c5f15898e2d28f9946436fec2b831d4eae

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

In Computer Graphics and Computer-Aided Design

In Computer Vision and Pattern Recognition

Citations in the corpus

ARCH: Animatable Reconstruction of Clothed Humans (2020, background): “There are multiple attempts to solve this task with video inputs [2, 3, 37, 1, 52], RGB-D data [53, 56] and in multi-view settings [5, 13, 14, 45, 46, 47, 48, 6].”
TailorNet: Predicting Clothing in 3D as a Function of Human Pose, Shape and Garment Style (2020, methodology): “One way to achieve realism is to capture real clothing on humans from images [6, 5, 3, 4, 19], dynamic scans [41, 39] or RGBD [52, 51] and re-target it to novel shapes, but this is limited to re-animating the same motion on a novel shape.”
VITON-HD: High-Resolution Virtual Try-On via Misalignment-Aware Normalization (2021, background): “There are two main categories for virtual try-on approaches: 3D model-based approaches [8, 27, 23, 22] and 2D image-based approaches [10, 31, 9, 36, 35, 5].”
DoubleFusion: Real-Time Capture of Human Performances with Inner Body Shapes from a Single Depth Sensor (2018, methodology): “[30] introduce ClothCap, which jointly estimates clothing geometry and body shape using separate meshes.”
BEHAVE: Dataset and Method for Tracking Human Object Interactions (2022, background): “The last decade has seen rapid progress in modelling the appearance of humans ranging from body pose, shape [52, 58, 60, 61, 81], faces [74] and even detailed clothing [5, 7, 11,57,65].”
Deep Volumetric Video From Very Sparse Multi-view Performance Capture (2018, methodology): “To reduce the computational cost for the inverse rendering problem in many of these approaches, Pons-Moll et al. [54] propose a multi-cloth 3D model to reconstruct both body and clothes from 4D scan sequences, estimating an unclothed body shape using [55, 56] and tracking the clothing over time.”
Detailed, Accurate, Human Shape Estimation from Clothed 3D Scan Sequences (2017, methodology): “Here we use the segmentation method in [30], see Sup.”
Implicit Functions in Feature Space for 3D Shape Reconstruction and Completion (2020, methodology): “Such implicit representation has been combined with the SMPL [41] body model to significantly increase tracking robustness and accuracy [78].”
VITON: An Image-Based Virtual Try-on Network (2017, methodology): “[35] utilized a multi-part 3D model of clothed bodies for clothing capture and retargeting.”
PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization (2019, background): “The use of motion cues has also been introduced as additional priors [47, 68].”
Multi-Garment Net: Learning to Dress 3D People From Images (2019, methodology): “For garment registration, we build on the ideas of ClothCap [47], which can register a subject specific multi-part model to a 4D scan sequence.”
Tex2Shape: Detailed Full Human Body Geometry From a Single Image (2019, methodology): “Body shape under clothing has been estimated without [59] and jointly with a separate clothing layer [38] from 3D scans and from RGB-D [45].”
DeepHuman: 3D Human Reconstruction From a Single Image (2019, background): “Image-based reconstruction of a human body is an important research topic for VR/AR content creation [7], image and video editing and re-enactment [19, 43], holoportation [40] and virtual dressing [42].”
Neural Unsigned Distance Fields for Implicit Function Learning (2020, methodology): “Meshes are common to represent humans and have been used for estimating pose, shape [38, 40, 41, 52, 75, 85] and clothing [4, 3, 12] from images, but topologies and detail are restricted by an underlying model like SMPL [47] or SMPL + Garments meshes (ClothCap [57], TailorNet [54] and GarNet [30], S”
Total Capture: A 3D Deformation Model for Tracking Faces, Hands, and Bodies (2018, background): “Since these body models often assume minimum clothing for subjects, explicit modeling for clothing is needed to capture clothed subjects [63, 39].”
Learning to Reconstruct People in Clothing From a Single RGB Camera (2019, background): “Shape and clothing have been recovered from RGB images [31, 15], depth [16], or scan data [56], but require manual intervention or clothing is limited to a pre-defined set of templates.”