Learning to Dress 3D People in Generative Clothing. Q Ma, J Yang, A Ranjan, S Pujades, G Pons-Moll, S Tang, MJ Black.
Date: June 2020. Source: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA. Abstract: 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 […]Read More
Learning Multi-human Optical Flow. A Ranjan, DT Hoffmann, D Tzionas et al.
Date: January 2020. Source: International Journal of Computer Vision 128, 873–890 (2020). https://doi.org/10.1007/s11263-019-01279-w. Abstract: The optical flow of humans is well known to be useful for the analysis of human action. Recent optical flow methods focus on training deep networks to approach the problem. However, the training data used by them does not cover the […]Read More
Learning to Dress 3D People in Generative Clothing. QL Ma, JL Yang, A Ranjan, S Pujades, G Pons-Moll, SY Tang, MJ Black.
Date: December 2019. Source: Cornell University Library – arXiv.org, Computer Vision and Pattern Recognition. Abstract: 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. […]Read More
The Influence of Visual Perspective on Body Size Estimation in Immersive Virtual Reality. A Thaler, S Pujades, JK Stefanucci, SH Creem-Regehr, J Tesch, MJ Black, and BJ Mohler
Date: September 2019. Source: ACM Symposium on Applied Perception 2019, University of Barcelona, Spain. Abstract: The creation of realistic self-avatars that users identify with is important for many virtual reality applications. However, current approaches for creating biometrically plausible avatars that represent a particular individual require expertise and are time-consuming. We investigated the visual perception of […]Read More
Capture, Learning, and Synthesis of 3D Speaking Styles. D Cudeiro, T Bolkart, C Laidlaw, A Ranjan, MJ Black.
Date: June 2019. Source: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA. Proceedings Page(s): 10093-10103. Abstract: Audio-driven 3D facial animation has been widely explored, but achieving realistic, human-like performance is still unsolved. This is due to the lack of available 3D datasets, models, and standard evaluation metrics. To address […]Read More
Expressive Body Capture: 3D Hands, Face, and Body from a Single Image. G Pavlakos, V Choutas, N Ghorbani, T Bolkart, AA Osman, D Tzionas, MJ Black.
Date: June 2019. Source: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA. Proceedings Page(s): 10967-10977. Abstract: To facilitate the analysis of human actions, interactions and emotions, we compute a 3D model of human body pose, hand pose, and facial expression from a single monocular image. To achieve this, we […]Read More
AMASS: Archive of Motion Capture as Surface Shapes. N Mahmood , N Ghorbani, NF Troje, G Pons-Moll, MJ Black.
Date: April 2019. Source: Cornell University Library – arXiv.org, Computer Vision and Pattern Recognition. Abstract: Large datasets are the cornerstone of recent advances in computer vision using deep learning. In contrast, existing human motion capture (mocap) datasets are small and the motions limited, hampering progress on learning models of human motion. While there are many […]Read More
Generating 3D Faces using Convolutional Mesh Autoencoders. A Ranjan, T Bolkart, S Sanyal, MJ Black.
Date: September 2018. Source: 15th European Conference on Computer Vision (ECCV), Munich, Germany, (pp 704-720). Abstract: Learned 3D representations of human faces are useful for computer vision problems such as 3D face tracking and reconstruction from images, as well as graphics applications such as character generation and animation. Traditional models learn a latent representation of […]Read More
Body size estimation of self and others in females varying in BMI. A Thaler, MN Geuss, SC Mölbert, KE Giel, S Streuber, J Romero, MJ Black, BJ Mohler.
Date: February 2018. Source: PLOS One. 13(2): e0192152. Abstract: Previous literature suggests that a disturbed ability to accurately identify own body size may contribute to overweight. Here, we investigated the influence of personal body size, indexed by body mass index (BMI), on body size estimation in a non-clinical population of females varying in BMI. We attempted […]Read More
Embodied Hands: Modeling and Capturing Hands and Bodies Together. J Romero, D Tzionas, MJ Black.
Date: November 2017. Source: ACM Transactions on Graphics (TOG) – Proceedings of ACM SIGGRAPH Asia 2017. Abstract: Humans move their hands and bodies together to communicate and solve tasks. Capturing and replicating such coordinated activity is critical for virtual characters that behave realistically. Surprisingly, most methods treat the 3D modeling and tracking of bodies and […]Read More
4D Movies Capture People in Clothing, Creating Realistic Virtual Try-on. M Black, G Pons-Moll, S Pujades.
Date: August 2017 Source: SIGGRAPH 2017, Los Angeles, CA, USA Overview: Researchers at the Max Planck Institute for Intelligent Systems (MPI-IS) have developed technology to digitally capture clothing on moving people, turn it into a 3D digital form, and dress virtual avatars with it. This new technology makes virtual clothing try-on practical. ClothCap uses 4D […]Read More
Detailed, accurate, human shape estimation from clothed 3D scan sequences. C Zhang, S Pujades, M Black, G Pons-Moll.
Date: March 2017. Source: researchgate.net (goo.gl/scholar/d1zUSr) Abstract: We address the problem of estimating human body shape from 3D scans over time. Reliable estimation of 3D body shape is necessary for many applications including virtual try-on, health monitoring, and avatar creation for virtual reality. Scanning bodies in minimal clothing, however, presents a practical barrier to these […]Read More
Appealing Female Avatars from 3D Body Scans: Perceptual Effects of Stylization. R Fleming, BJ Mohler, J Romero, MJ Black, M Breidt.
Date: March 2016. Source: Semantic Scholar.org. Abstract: Advances in 3D scanning technology allow us to create realistic virtual avatars from full body 3D scan data. However, negative reactions to some realistic computer generated humans suggest that this approach might not always provide the most appealing results. Using styles derived from existing popular character designs, we […]Read More
Dyna: A Model of Dynamic Human Shape in Motion. Gerard Pons-Moll, Javier Romero, Naureen Mahmood, Michael J. Black.
Date: August 2015. Source: SIGGRAPH 2015. Journal ACM Transactions on Graphics (TOG), Volume 34 Issue 4, Article No. 120. SIGGRAPH Presentation: https://youtu.be/mWthea2K8-Q Abstract: To look human, digital full-body avatars need to have soft-tissue deformations like those of real people. We learn a model of soft-tissue deformations from examples using a high-resolution 4D capture system and […]Read More
Can I Recognize My Body’s Weight? The Influence of Shape and Texture on the Perception of Self. I Piryankova, J Stefanucci, J Romero, S de la Rosa, M Black, B Mohler.
Date: September 2014. Source: ACM Transactions on Applied Perception, Vol. 11, No. 3, Article 13. Abstract: The goal of this research was to investigate women’s sensitivity to changes in their perceived weight by altering the body mass index (BMI) of the participants’ personalized avatars displayed on a large-screen immersive display. We created the personalized avatars […]Read More