1,099 research outputs found
Automatic 3D facial modelling with deformable models.
Facial modelling and animation has been an active research subject in computer graphics since the 1970s. Due to extremely complex biomechanical structures of human faces and peoples visual familiarity with human faces, modelling and animating realistic human faces is still one of greatest challenges in computer graphics. Since we are so familiar with human faces and very sensitive to unnatural subtle changes in human faces, it usually requires a tremendous amount of artistry and manual work to create a convincing facial model and animation. There is a clear need of developing automatic techniques for facial modelling in order to reduce manual labouring. In order to obtain a realistic facial model of an individual, it is now common to make use of 3D scanners to capture range scans from the individual and then fit a template to the range scans. However, most existing template-fitting methods require manually selected landmarks to warp the template to the range scans. It would be tedious to select landmarks by hand over a large set of range scans. Another way to reduce repeated work is synthesis by reusing existing data. One example is expression cloning, which copies facial expression from one face to another instead of creating them from scratch. This aim of this study is to develop a fully automatic framework for template-based facial modelling, facial expression transferring and facial expression tracking from range scans. In this thesis, the author developed an extension of the iterative closest points (ICP) algorithm, which is able to match a template with range scans in different scales, and a deformable model, which can be used to recover the shapes of range scans and to establish correspondences between facial models. With the registration method and the deformable model, the author proposed a fully automatic approach to reconstructing facial models and textures from range scans without re-quiring any manual interventions. In order to reuse existing data for facial modelling, the author formulated and solved the problem of facial expression transferring in the framework of discrete differential geometry. The author also applied his methods to face tracking for 4D range scans. The results demonstrated the robustness of the registration method and the capabilities of the deformable model. A number of possible directions for future work were pointed out
Linear Facial Expression Transfer With Active Appearance Models
The issue of transferring facial expressions from one person's face to another's has been an area of interest for the movie industry and the computer graphics community for quite some time. In recent years, with the proliferation of online image and video collections and web applications, such as Google Street View, the question of preserving privacy through face de-identification has gained interest in the computer vision community. In this paper, we focus on the problem of real-time dynamic facial expression transfer using an Active Appearance Model framework. We provide a theoretical foundation for a generalisation of two well-known expression transfer methods and demonstrate the improved visual quality of the proposed linear extrapolation transfer method on examples of face swapping and expression transfer using the AVOZES data corpus. Realistic talking faces can be generated in real-time at low computational cost
Recombinator Networks: Learning Coarse-to-Fine Feature Aggregation
Deep neural networks with alternating convolutional, max-pooling and
decimation layers are widely used in state of the art architectures for
computer vision. Max-pooling purposefully discards precise spatial information
in order to create features that are more robust, and typically organized as
lower resolution spatial feature maps. On some tasks, such as whole-image
classification, max-pooling derived features are well suited; however, for
tasks requiring precise localization, such as pixel level prediction and
segmentation, max-pooling destroys exactly the information required to perform
well. Precise localization may be preserved by shallow convnets without pooling
but at the expense of robustness. Can we have our max-pooled multi-layered cake
and eat it too? Several papers have proposed summation and concatenation based
methods for combining upsampled coarse, abstract features with finer features
to produce robust pixel level predictions. Here we introduce another model ---
dubbed Recombinator Networks --- where coarse features inform finer features
early in their formation such that finer features can make use of several
layers of computation in deciding how to use coarse features. The model is
trained once, end-to-end and performs better than summation-based
architectures, reducing the error from the previous state of the art on two
facial keypoint datasets, AFW and AFLW, by 30\% and beating the current
state-of-the-art on 300W without using extra data. We improve performance even
further by adding a denoising prediction model based on a novel convnet
formulation.Comment: accepted in CVPR 201
Occlusion Coherence: Detecting and Localizing Occluded Faces
The presence of occluders significantly impacts object recognition accuracy.
However, occlusion is typically treated as an unstructured source of noise and
explicit models for occluders have lagged behind those for object appearance
and shape. In this paper we describe a hierarchical deformable part model for
face detection and landmark localization that explicitly models part occlusion.
The proposed model structure makes it possible to augment positive training
data with large numbers of synthetically occluded instances. This allows us to
easily incorporate the statistics of occlusion patterns in a discriminatively
trained model. We test the model on several benchmarks for landmark
localization and detection including challenging new data sets featuring
significant occlusion. We find that the addition of an explicit occlusion model
yields a detection system that outperforms existing approaches for occluded
instances while maintaining competitive accuracy in detection and landmark
localization for unoccluded instances
Real-Time Facial Expression Transfer with Single Video Camera
Facial expression transfer is currently an active research field. However, 2D image wrapping based methods suffer from depth ambiguity and specific hardware is required for depth-based methods to work. We present a novel markerless, real time online facial transfer method that requires only a single video camera. Our method adapts a model to user specific facial data, computes expression variances in real time and rapidly transfers them to another target. Our method can be applied to videos without prior camera calibration and focal adjustment. It enables realistic online facial expression editing and performance transferring in many scenarios, such as: video conference; news broadcasting; lip-syncing for song performances; etc. With a low computational demand and hardware requirement, our method tracks a single user at an average of 38 fps. Our tracking method runs smoothly in web browsers despite their slower execution speed
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