6,872 research outputs found
HeadOn: Real-time Reenactment of Human Portrait Videos
We propose HeadOn, the first real-time source-to-target reenactment approach
for complete human portrait videos that enables transfer of torso and head
motion, face expression, and eye gaze. Given a short RGB-D video of the target
actor, we automatically construct a personalized geometry proxy that embeds a
parametric head, eye, and kinematic torso model. A novel real-time reenactment
algorithm employs this proxy to photo-realistically map the captured motion
from the source actor to the target actor. On top of the coarse geometric
proxy, we propose a video-based rendering technique that composites the
modified target portrait video via view- and pose-dependent texturing, and
creates photo-realistic imagery of the target actor under novel torso and head
poses, facial expressions, and gaze directions. To this end, we propose a
robust tracking of the face and torso of the source actor. We extensively
evaluate our approach and show significant improvements in enabling much
greater flexibility in creating realistic reenacted output videos.Comment: Video: https://www.youtube.com/watch?v=7Dg49wv2c_g Presented at
Siggraph'1
Visibility Constrained Generative Model for Depth-based 3D Facial Pose Tracking
In this paper, we propose a generative framework that unifies depth-based 3D
facial pose tracking and face model adaptation on-the-fly, in the unconstrained
scenarios with heavy occlusions and arbitrary facial expression variations.
Specifically, we introduce a statistical 3D morphable model that flexibly
describes the distribution of points on the surface of the face model, with an
efficient switchable online adaptation that gradually captures the identity of
the tracked subject and rapidly constructs a suitable face model when the
subject changes. Moreover, unlike prior art that employed ICP-based facial pose
estimation, to improve robustness to occlusions, we propose a ray visibility
constraint that regularizes the pose based on the face model's visibility with
respect to the input point cloud. Ablation studies and experimental results on
Biwi and ICT-3DHP datasets demonstrate that the proposed framework is effective
and outperforms completing state-of-the-art depth-based methods
A graphical model based solution to the facial feature point tracking problem
In this paper a facial feature point tracker that is motivated by applications
such as human-computer interfaces and facial expression analysis systems is
proposed. The proposed tracker is based on a graphical model framework. The
facial features are tracked through video streams by incorporating statistical relations in time as well as spatial relations between feature points. By exploiting the spatial relationships between feature points, the proposed method provides robustness in real-world conditions such as arbitrary head movements and occlusions. A Gabor feature-based occlusion detector is developed and used to handle occlusions. The performance of the proposed tracker has been evaluated
on real video data under various conditions including occluded facial gestures
and head movements. It is also compared to two popular methods, one based
on Kalman filtering exploiting temporal relations, and the other based on active
appearance models (AAM). Improvements provided by the proposed approach
are demonstrated through both visual displays and quantitative analysis
Expressive Body Capture: 3D Hands, Face, and Body from a Single Image
To facilitate the analysis of human actions, interactions and emotions, we
compute a 3D model of human body pose, hand pose, and facial expression from a
single monocular image. To achieve this, we use thousands of 3D scans to train
a new, unified, 3D model of the human body, SMPL-X, that extends SMPL with
fully articulated hands and an expressive face. Learning to regress the
parameters of SMPL-X directly from images is challenging without paired images
and 3D ground truth. Consequently, we follow the approach of SMPLify, which
estimates 2D features and then optimizes model parameters to fit the features.
We improve on SMPLify in several significant ways: (1) we detect 2D features
corresponding to the face, hands, and feet and fit the full SMPL-X model to
these; (2) we train a new neural network pose prior using a large MoCap
dataset; (3) we define a new interpenetration penalty that is both fast and
accurate; (4) we automatically detect gender and the appropriate body models
(male, female, or neutral); (5) our PyTorch implementation achieves a speedup
of more than 8x over Chumpy. We use the new method, SMPLify-X, to fit SMPL-X to
both controlled images and images in the wild. We evaluate 3D accuracy on a new
curated dataset comprising 100 images with pseudo ground-truth. This is a step
towards automatic expressive human capture from monocular RGB data. The models,
code, and data are available for research purposes at
https://smpl-x.is.tue.mpg.de.Comment: To appear in CVPR 201
MoFA: Model-based Deep Convolutional Face Autoencoder for Unsupervised Monocular Reconstruction
In this work we propose a novel model-based deep convolutional autoencoder
that addresses the highly challenging problem of reconstructing a 3D human face
from a single in-the-wild color image. To this end, we combine a convolutional
encoder network with an expert-designed generative model that serves as
decoder. The core innovation is our new differentiable parametric decoder that
encapsulates image formation analytically based on a generative model. Our
decoder takes as input a code vector with exactly defined semantic meaning that
encodes detailed face pose, shape, expression, skin reflectance and scene
illumination. Due to this new way of combining CNN-based with model-based face
reconstruction, the CNN-based encoder learns to extract semantically meaningful
parameters from a single monocular input image. For the first time, a CNN
encoder and an expert-designed generative model can be trained end-to-end in an
unsupervised manner, which renders training on very large (unlabeled) real
world data feasible. The obtained reconstructions compare favorably to current
state-of-the-art approaches in terms of quality and richness of representation.Comment: International Conference on Computer Vision (ICCV) 2017 (Oral), 13
page
EgoFace: Egocentric Face Performance Capture and Videorealistic Reenactment
Face performance capture and reenactment techniques use multiple cameras and sensors, positioned at a distance from the face or mounted on heavy wearable devices. This limits their applications in mobile and outdoor environments. We present EgoFace, a radically new lightweight setup for face performance capture and front-view videorealistic reenactment using a single egocentric RGB camera. Our lightweight setup allows operations in uncontrolled environments, and lends itself to telepresence applications such as video-conferencing from dynamic environments. The input image is projected into a low dimensional latent space of the facial expression parameters. Through careful adversarial training of the parameter-space synthetic rendering, a videorealistic animation is produced. Our problem is challenging as the human visual system is sensitive to the smallest face irregularities that could occur in the final results. This sensitivity is even stronger for video results. Our solution is trained in a pre-processing stage, through a supervised manner without manual annotations. EgoFace captures a wide variety of facial expressions, including mouth movements and asymmetrical expressions. It works under varying illuminations, background, movements, handles people from different ethnicities and can operate in real time
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