5,493 research outputs found
Generating 3D faces using Convolutional Mesh Autoencoders
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 a face using linear subspaces or
higher-order tensor generalizations. Due to this linearity, they can not
capture extreme deformations and non-linear expressions. To address this, we
introduce a versatile model that learns a non-linear representation of a face
using spectral convolutions on a mesh surface. We introduce mesh sampling
operations that enable a hierarchical mesh representation that captures
non-linear variations in shape and expression at multiple scales within the
model. In a variational setting, our model samples diverse realistic 3D faces
from a multivariate Gaussian distribution. Our training data consists of 20,466
meshes of extreme expressions captured over 12 different subjects. Despite
limited training data, our trained model outperforms state-of-the-art face
models with 50% lower reconstruction error, while using 75% fewer parameters.
We also show that, replacing the expression space of an existing
state-of-the-art face model with our autoencoder, achieves a lower
reconstruction error. Our data, model and code are available at
http://github.com/anuragranj/com
Neural 3D Morphable Models: Spiral Convolutional Networks for 3D Shape Representation Learning and Generation
Generative models for 3D geometric data arise in many important applications
in 3D computer vision and graphics. In this paper, we focus on 3D deformable
shapes that share a common topological structure, such as human faces and
bodies. Morphable Models and their variants, despite their linear formulation,
have been widely used for shape representation, while most of the recently
proposed nonlinear approaches resort to intermediate representations, such as
3D voxel grids or 2D views. In this work, we introduce a novel graph
convolutional operator, acting directly on the 3D mesh, that explicitly models
the inductive bias of the fixed underlying graph. This is achieved by enforcing
consistent local orderings of the vertices of the graph, through the spiral
operator, thus breaking the permutation invariance property that is adopted by
all the prior work on Graph Neural Networks. Our operator comes by construction
with desirable properties (anisotropic, topology-aware, lightweight,
easy-to-optimise), and by using it as a building block for traditional deep
generative architectures, we demonstrate state-of-the-art results on a variety
of 3D shape datasets compared to the linear Morphable Model and other graph
convolutional operators.Comment: to appear at ICCV 201
Dynamic facial expressions of emotion transmit an evolving hierarchy of signals over time
Designed by biological and social evolutionary pressures, facial expressions of emotion comprise specific facial movements to support a near-optimal system of signaling and decoding. Although highly dynamical, little is known about the form and function of facial expression temporal dynamics. Do facial expressions transmit diagnostic signals simultaneously to optimize categorization of the six classic emotions, or sequentially to support a more complex communication system of successive categorizations over time? Our data support the latter. Using a combination of perceptual expectation modeling, information theory, and Bayesian classifiers, we show that dynamic facial expressions of emotion transmit an evolving hierarchy of “biologically basic to socially specific” information over time. Early in the signaling dynamics, facial expressions systematically transmit few, biologically rooted face signals supporting the categorization of fewer elementary categories (e.g., approach/avoidance). Later transmissions comprise more complex signals that support categorization of a larger number of socially specific categories (i.e., the six classic emotions). Here, we show that dynamic facial expressions of emotion provide a sophisticated signaling system, questioning the widely accepted notion that emotion communication is comprised of six basic (i.e., psychologically irreducible) categories, and instead suggesting four
Human motion modeling and simulation by anatomical approach
To instantly generate desired infinite realistic human motion is still a great challenge in virtual human simulation. In this paper, the novel emotion effected motion classification and anatomical motion classification are presented, as well as motion capture and parameterization methods. The framework for a novel anatomical approach to model human motion in a HTR (Hierarchical Translations and Rotations) file format is also described. This novel anatomical approach in human motion modelling has the potential to generate desired infinite human motion from a compact motion database. An architecture for the real-time generation of new motions is also propose
Multilinear Wavelets: A Statistical Shape Space for Human Faces
We present a statistical model for D human faces in varying expression,
which decomposes the surface of the face using a wavelet transform, and learns
many localized, decorrelated multilinear models on the resulting coefficients.
Using this model we are able to reconstruct faces from noisy and occluded D
face scans, and facial motion sequences. Accurate reconstruction of face shape
is important for applications such as tele-presence and gaming. The localized
and multi-scale nature of our model allows for recovery of fine-scale detail
while retaining robustness to severe noise and occlusion, and is
computationally efficient and scalable. We validate these properties
experimentally on challenging data in the form of static scans and motion
sequences. We show that in comparison to a global multilinear model, our model
better preserves fine detail and is computationally faster, while in comparison
to a localized PCA model, our model better handles variation in expression, is
faster, and allows us to fix identity parameters for a given subject.Comment: 10 pages, 7 figures; accepted to ECCV 201
Data-Driven Shape Analysis and Processing
Data-driven methods play an increasingly important role in discovering
geometric, structural, and semantic relationships between 3D shapes in
collections, and applying this analysis to support intelligent modeling,
editing, and visualization of geometric data. In contrast to traditional
approaches, a key feature of data-driven approaches is that they aggregate
information from a collection of shapes to improve the analysis and processing
of individual shapes. In addition, they are able to learn models that reason
about properties and relationships of shapes without relying on hard-coded
rules or explicitly programmed instructions. We provide an overview of the main
concepts and components of these techniques, and discuss their application to
shape classification, segmentation, matching, reconstruction, modeling and
exploration, as well as scene analysis and synthesis, through reviewing the
literature and relating the existing works with both qualitative and numerical
comparisons. We conclude our report with ideas that can inspire future research
in data-driven shape analysis and processing.Comment: 10 pages, 19 figure
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