SUPR: A Sparse Unified Part-Based Human Representation. AAA Osman, T Bolkart, D Tzionas, MJ Black.
MPI-IS trains SUPR (2022), a full-body, hand, head, and foot model, with 3dMD’s real-world 4D scan data, presented at ECCV 2022.
MimicME: A Large Scale Diverse 4D Database for Facial Expression Analysis. A Papaioannou et al.
Imperial College London captures real-world MimicMe, a 4,700-subject 4D facial expression database, with 3dMD, presented at ECCV 2022.
Identity-Disentangled Neural Deformation Model for Dynamic Meshes (3DH). B Xu, L Ma, Y Ye, T Schmidt, CD Twigg, S Lovegrove.
Meta Reality Labs and Imperial College London build a neural hand deformation model with 3dMD’s real-world 3DH scan data, 2021.
Shape My Face: Registering 3D Face Scans by Surface-to-Surface Translation. M Bahri, E O’ Sullivan, S Gong, F Liu, X Liu, MM Bronstein, S Zafeiriou.
Imperial College London builds Shape My Face (2020), a 3D face registration model, with 3dMD’s real-world training and test data.
Learning 3D Faces from Photo-Realistic Facial Synthesis. R Wang, C-F. Chen, H Peng, X Liu, X Li.
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BLSM: A Bone-Level Skinned Model of the Human Mesh. H Wang, RA Guler, I Kokkinos, G Papandreou, S Zafeiriou.
Imperial College London builds BLSM (2020), a bone-level body mesh model, with 3dMD’s real-world training and evaluation data, presented at ECCV 2020.
Learning to Dress 3D People in Generative Clothing (CAPE). Q Ma, J Yang, A Ranjan, S Pujades, G Pons-Moll, S Tang, MJ Black.
MPI-IS trains its 2020 CAPE generative model for dressing 3D people in clothing with 3dMD’s 4D real-world body data.
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…
Digital Twin: Acquiring High-Fidelity 3D Avatar from a Single Image. R Wang, CF Chen, H Peng, X Liu, O Liu, X Li.
Date: December 2019. Source: Cornell University Library – arXiv.org, Computer Vision and Pattern Recognition. Abstract: We present an approach to generate high fidelity 3D face avatar with a high-resolution UV texture map from a single image. To estimate the face geometry, we use a deep neural network to directly predict vertex coordinates of the 3D…
The Menpo Benchmark for Multi-pose 2D and 3D Facial Landmark Localisation and Tracking. J Deng, A Roussos, G Chrysos et al.
Date: November 2019. Source: International Journal of Computer Vision, Volume 127, pages 599–624, https://doi.org/10.1007/s11263-018-1134-y. Abstract: In this article, we present the Menpo 2D and Menpo 3D benchmarks, two new datasets for multi-pose 2D and 3D facial landmark localisation and tracking. In contrast to the previous benchmarks such as 300W and 300VW, the proposed benchmarks contain…