medical

3D Face Modelling

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

These leaderboards are used to track progress in 3d-face-modelling-2

Trend

Dataset

Best Model

Actions

Voxceleb-3D

LFW

Libraries

i

Use these libraries to find 3d-face-modelling-2 models and implementations

Datasets

LFW

Human Optical Flow

FaMoS

Voxceleb-3D

Subtasks

Continuous Control Facial Recognition and Modelling

Most implemented papers

Learning a model of facial shape and expression from 4D scans

Michael J. Black, J. Romero, Timo Bolkart, Tianye Li, Hao Li•Sun Nov 19 2017

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.

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Synergy between 3DMM and 3D Landmarks for Accurate 3D Facial Geometry

Cho-Ying Wu, U. Neumann, Qiangeng Xu•Mon Oct 18 2021

This work studies learning from a synergy process of 3D Morphable Models (3DMM) and3D facial landmarks to predict complete 3D facial geometry, including 3D alignment, face orientation, and 3D face modeling.

75 0

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Towards Fast, Accurate and Stable 3D Dense Face Alignment

Zhen Lei, S. Li, Yang Yang, Xiangyu Zhu, Jianzhu Guo•Sat Aug 22 2020

A novel regression framework which makes a balance among speed, accuracy and stability, and a meta-joint optimization strategy to dynamically regress a small set of 3DMM parameters, which greatly enhances speed and accuracy simultaneously.

466 0

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DeepFace: Closing the Gap to Human-Level Performance in Face Verification

Marc'Aurelio Ranzato, Ming Yang, Lior Wolf, Yaniv Taigman•Sat May 31 2014

In modern face recognition, the conventional pipeline consists of four stages: detect => align => represent => classify. We revisit both the alignment step and the representation step by employing explicit 3D face modeling in order to apply a piecewise affine transformation, and derive a face representation from a nine-layer deep neural network. This deep network involves more than 120 million parameters using several locally connected layers without weight sharing, rather than the standard convolutional layers. Thus we trained it on the largest facial dataset to-date, an identity labeled dataset of four million facial images belonging to more than 4, 000 identities. The learned representations coupling the accurate model-based alignment with the large facial database generalize remarkably well to faces in unconstrained environments, even with a simple classifier. Our method reaches an accuracy of 97.35% on the Labeled Faces in the Wild (LFW) dataset, reducing the error of the current state of the art by more than 27%, closely approaching human-level performance.

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Generating 3D faces using Convolutional Mesh Autoencoders

Michael J. Black, Soubhik Sanyal, Timo Bolkart, Anurag Ranjan•Wed Jul 25 2018

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.

612 0

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Learning an animatable detailed 3D face model from in-the-wild images

Michael J. Black, Timo Bolkart, Haiwen Feng, Yao Feng•Sun Dec 06 2020

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.

735 0

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Multilinear Wavelets: A Statistical Shape Space for Human Faces

Timo Bolkart, Alan Brunton, Stefanie Wuhrer•Sun Jan 12 2014

A statistical model for 3D human faces in varying expression is presented, which decomposes the surface of the face using a wavelet transform, and learns many localized, decorrelated multilinear models on the resulting coefficients.

98 0

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Review of statistical shape spaces for 3D data with comparative analysis for human faces

Timo Bolkart, Alan Brunton, Augusto Salazar, Stefanie Wuhrer•Thu Sep 27 2012

This paper reviews how different types of models have been used in the literature, then proceeds to define the models and analyze them theoretically, in terms of both their statistical and computational aspects, and performs extensive experimental comparison on the task of model fitting.

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3D faces in motion: Fully automatic registration and statistical analysis

Timo Bolkart, Stefanie Wuhrer•Sat Jan 31 2015

This paper proposes an approach to robustly compute correspondences between a large set of facial motion sequences in a fully automatic way using a multilinear model as statistical prior and synthesizes new motion sequences and performs expression recognition.

30 0

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