292 research outputs found
Facial Expression Retargeting from Human to Avatar Made Easy
Facial expression retargeting from humans to virtual characters is a useful
technique in computer graphics and animation. Traditional methods use markers
or blendshapes to construct a mapping between the human and avatar faces.
However, these approaches require a tedious 3D modeling process, and the
performance relies on the modelers' experience. In this paper, we propose a
brand-new solution to this cross-domain expression transfer problem via
nonlinear expression embedding and expression domain translation. We first
build low-dimensional latent spaces for the human and avatar facial expressions
with variational autoencoder. Then we construct correspondences between the two
latent spaces guided by geometric and perceptual constraints. Specifically, we
design geometric correspondences to reflect geometric matching and utilize a
triplet data structure to express users' perceptual preference of avatar
expressions. A user-friendly method is proposed to automatically generate
triplets for a system allowing users to easily and efficiently annotate the
correspondences. Using both geometric and perceptual correspondences, we
trained a network for expression domain translation from human to avatar.
Extensive experimental results and user studies demonstrate that even
nonprofessional users can apply our method to generate high-quality facial
expression retargeting results with less time and effort.Comment: IEEE Transactions on Visualization and Computer Graphics (TVCG), to
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Accurate and Interpretable Solution of the Inverse Rig for Realistic Blendshape Models with Quadratic Corrective Terms
We propose a new model-based algorithm solving the inverse rig problem in
facial animation retargeting, exhibiting higher accuracy of the fit and
sparser, more interpretable weight vector compared to SOTA. The proposed method
targets a specific subdomain of human face animation - highly-realistic
blendshape models used in the production of movies and video games. In this
paper, we formulate an optimization problem that takes into account all the
requirements of targeted models. Our objective goes beyond a linear blendshape
model and employs the quadratic corrective terms necessary for correctly
fitting fine details of the mesh. We show that the solution to the proposed
problem yields highly accurate mesh reconstruction even when general-purpose
solvers, like SQP, are used. The results obtained using SQP are highly accurate
in the mesh space but do not exhibit favorable qualities in terms of weight
sparsity and smoothness, and for this reason, we further propose a novel
algorithm relying on a MM technique. The algorithm is specifically suited for
solving the proposed objective, yielding a high-accuracy mesh fit while
respecting the constraints and producing a sparse and smooth set of weights
easy to manipulate and interpret by artists. Our algorithm is benchmarked with
SOTA approaches, and shows an overall superiority of the results, yielding a
smooth animation reconstruction with a relative improvement up to 45 percent in
root mean squared mesh error while keeping the cardinality comparable with
benchmark methods. This paper gives a comprehensive set of evaluation metrics
that cover different aspects of the solution, including mesh accuracy, sparsity
of the weights, and smoothness of the animation curves, as well as the
appearance of the produced animation, which human experts evaluated
Easy Rigging of Face by Automatic Registration and Transfer of Skinning Parameters
International audiencePreparing a facial mesh to be animated requires a laborious manualrigging process. The rig specifies how the input animation datadeforms the surface and allows artists to manipulate a character.We present a method that automatically rigs a facial mesh based onRadial Basis Functions and linear blend skinning approach.Our approach transfers the skinning parameters (feature points andtheir envelopes, ie. point-vertex weights),of a reference facial mesh (source) - already rigged - tothe chosen facial mesh (target) by computing an automaticregistration between the two meshes.There is no need to manually mark the correspondence between thesource and target mesh.As a result, inexperienced artists can automatically rig facial meshes and startright away animating their 3D characters, driven for instanceby motion capture data
Zero-shot Pose Transfer for Unrigged Stylized 3D Characters
Transferring the pose of a reference avatar to stylized 3D characters of
various shapes is a fundamental task in computer graphics. Existing methods
either require the stylized characters to be rigged, or they use the stylized
character in the desired pose as ground truth at training. We present a
zero-shot approach that requires only the widely available deformed
non-stylized avatars in training, and deforms stylized characters of
significantly different shapes at inference. Classical methods achieve strong
generalization by deforming the mesh at the triangle level, but this requires
labelled correspondences. We leverage the power of local deformation, but
without requiring explicit correspondence labels. We introduce a
semi-supervised shape-understanding module to bypass the need for explicit
correspondences at test time, and an implicit pose deformation module that
deforms individual surface points to match the target pose. Furthermore, to
encourage realistic and accurate deformation of stylized characters, we
introduce an efficient volume-based test-time training procedure. Because it
does not need rigging, nor the deformed stylized character at training time,
our model generalizes to categories with scarce annotation, such as stylized
quadrupeds. Extensive experiments demonstrate the effectiveness of the proposed
method compared to the state-of-the-art approaches trained with comparable or
more supervision. Our project page is available at
https://jiashunwang.github.io/ZPTComment: CVPR 202
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