21,223 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
Hand2Face: Automatic Synthesis and Recognition of Hand Over Face Occlusions
A person's face discloses important information about their affective state.
Although there has been extensive research on recognition of facial
expressions, the performance of existing approaches is challenged by facial
occlusions. Facial occlusions are often treated as noise and discarded in
recognition of affective states. However, hand over face occlusions can provide
additional information for recognition of some affective states such as
curiosity, frustration and boredom. One of the reasons that this problem has
not gained attention is the lack of naturalistic occluded faces that contain
hand over face occlusions as well as other types of occlusions. Traditional
approaches for obtaining affective data are time demanding and expensive, which
limits researchers in affective computing to work on small datasets. This
limitation affects the generalizability of models and deprives researchers from
taking advantage of recent advances in deep learning that have shown great
success in many fields but require large volumes of data. In this paper, we
first introduce a novel framework for synthesizing naturalistic facial
occlusions from an initial dataset of non-occluded faces and separate images of
hands, reducing the costly process of data collection and annotation. We then
propose a model for facial occlusion type recognition to differentiate between
hand over face occlusions and other types of occlusions such as scarves, hair,
glasses and objects. Finally, we present a model to localize hand over face
occlusions and identify the occluded regions of the face.Comment: Accepted to International Conference on Affective Computing and
Intelligent Interaction (ACII), 201
Automatic facial expression tracking for 4D range scans
This paper presents a fully automatic approach of spatio-temporal facial expression tracking for 4D range scans without any manual interventions (such as specifying landmarks). The approach consists of three steps: rigid registration, facial model reconstruction, and facial expression tracking. A Scaling Iterative Closest Points (SICP) algorithm is introduced to compute the optimal rigid registration between a template facial model and a range scan with consideration of the scale problem. A deformable model, physically based on thin shells, is proposed to faithfully reconstruct the facial surface and texture from that range data. And then the reconstructed facial model is used to track facial expressions presented in a sequence of range scans by the deformable model
Tex2Shape: Detailed Full Human Body Geometry From a Single Image
We present a simple yet effective method to infer detailed full human body shape from only a single photograph. Our model can infer full-body shape including face, hair, and clothing including wrinkles at interactive frame-rates. Results feature details even on parts that are occluded in the input image. Our main idea is to turn shape regression into an aligned image-to-image translation problem. The input to our method is a partial texture map of the visible region obtained from off-the-shelf methods. From a partial texture, we estimate detailed normal and vector displacement maps, which can be applied to a low-resolution smooth body model to add detail and clothing. Despite being trained purely with synthetic data, our model generalizes well to real-world photographs. Numerous results demonstrate the versatility and robustness of our method
Tex2Shape: Detailed Full Human Body Geometry From a Single Image
We present a simple yet effective method to infer detailed full human body
shape from only a single photograph. Our model can infer full-body shape
including face, hair, and clothing including wrinkles at interactive
frame-rates. Results feature details even on parts that are occluded in the
input image. Our main idea is to turn shape regression into an aligned
image-to-image translation problem. The input to our method is a partial
texture map of the visible region obtained from off-the-shelf methods. From a
partial texture, we estimate detailed normal and vector displacement maps,
which can be applied to a low-resolution smooth body model to add detail and
clothing. Despite being trained purely with synthetic data, our model
generalizes well to real-world photographs. Numerous results demonstrate the
versatility and robustness of our method
- …