• Publications
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Expressive Body Capture: 3D Hands, Face, and Body From a Single Image
TLDR
This work uses the new method, SMPLify-X, to fit SMPL-X to both controlled images and images in the wild, and evaluates 3D accuracy on a new curated dataset comprising 100 images with pseudo ground-truth.
Generating 3D faces using Convolutional Mesh Autoencoders
TLDR
This work introduces a versatile model that learns a non-linear representation of a face using spectral convolutions on a mesh surface and shows that, replacing the expression space of an existing state-of-the-art face model with this model, achieves a lower reconstruction error.
Learning a model of facial shape and expression from 4D scans
TLDR
Faces Learned with an Articulated Model and Expressions is low-dimensional but more expressive than the FaceWarehouse model and the Basel Face Model and is compared to these models by fitting them to static 3D scans and 4D sequences using the same optimization method.
Capture, Learning, and Synthesis of 3D Speaking Styles
TLDR
A unique 4D face dataset with about 29 minutes of 4D scans captured at 60 fps and synchronized audio from 12 speakers is introduced and VOCA (Voice Operated Character Animation) is learned, the only realistic 3D facial animation model that is readily applicable to unseen subjects without retargeting.
Learning to Regress 3D Face Shape and Expression From an Image Without 3D Supervision
TLDR
To train a network without any 2D-to-3D supervision, RingNet is presented, which learns to compute 3D face shape from a single image and achieves invariance to expression by representing the face using the FLAME model.
Fitting a 3D Morphable Model to Edges: A Comparison Between Hard and Soft Correspondences
TLDR
This work proposes a fully automatic method for fitting a 3D morphable model to single face images in arbitrary pose and lighting and demonstrates that this is superior to previous work that uses soft correspondences to form an edge-derived cost surface that is minimised by nonlinear optimisation.
Monocular Expressive Body Regression through Body-Driven Attention
TLDR
ExPose estimates expressive 3D humans more accurately than existing optimization methods at a small fraction of the computational cost by introducing ExPose (EXpressive POse and Shape rEgression), which directly regresses the body, face, and hands, in SMPL-X format, from an RGB image.
STAR: Sparse Trained Articulated Human Body Regressor
TLDR
This work defines per-joint pose correctives and learns the subset of mesh vertices that are influenced by each joint movement that results in more realistic deformations and significantly reduces the number of model parameters to 20% of SMPL.
Learning an animatable detailed 3D face model from in-the-wild images
TLDR
This work presents the first approach that regresses 3D face shape and animatable details that are specific to an individual but change with expression, and introduces a novel detail-consistency loss that disentangles person-specific details from expression-dependent wrinkles.
3D Morphable Face Models—Past, Present, and Future
TLDR
A detailed survey of 3D Morphable Face Models over the 20 years since they were first proposed is provided, identifying unsolved challenges, proposing directions for future research, and highlighting the broad range of current and future applications.
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