We consider the problem of face swapping in images, where an input identity is transformed into a target identity while preserving pose, facial expression, and lighting. To perform this mapping, we use convolutional neural networks trained to capture the appearance of the target identity from an unstructured collection of his her photographs.This approach is enabled by framing the face swapping problem in terms of style transfer, where the goal is to render an image in the style of another one. Building on recent advances in this area, we devise a new loss function that enables the network to produce highly photorealistic results. By combining neural networks with simple pre- and post-processing steps, we aim at making face swap work in real-time with no input from the user.
A more difficult problem was addressed by Dale al @cite_15 . Their work focused on the replacement of faces in videos, where video footage of two subjects performing similar roles are available. Compared to static images, sequential data poses extra difficulties of temporal alignment, tracking facial performance and ensuring temporal consistency of the resulting footage. The resulting system is complex and still requires a substantial amount of time and user guidance.
@cite_15
1995712965
We present a method for replacing facial performances in video. Our approach accounts for differences in identity, visual appearance, speech, and timing between source and target videos. Unlike prior work, it does not require substantial manual operation or complex acquisition hardware, only single-camera video. We use a 3D multilinear model to track the facial performance in both videos. Using the corresponding 3D geometry, we warp the source to the target face and retime the source to match the target performance. We then compute an optimal seam through the video volume that maintains temporal consistency in the final composite. We showcase the use of our method on a variety of examples and present the result of a user study that suggests our results are difficult to distinguish from real video footage. We present a system for quick and easy manipulation of the body shape and proportions of a human actor in arbitrary video footage. The approach is based on a morphable model of 3D human shape and pose that was learned from laser scans of real people. The algorithm commences by spatio-temporally fitting the pose and shape of this model to the actor in either single-view or multi-view video footage. Once the model has been fitted, semantically meaningful attributes of body shape, such as height, weight or waist girth, can be interactively modified by the user. The changed proportions of the virtual human model are then applied to the actor in all video frames by performing an image-based warping. By this means, we can now conveniently perform spatio-temporal reshaping of human actors in video footage which we show on a variety of video sequences. We present a real-time deep learning framework for video-based facial performance capture---the dense 3D tracking of an actor's face given a monocular video. Our pipeline begins with accurately capturing a subject using a high-end production facial capture pipeline based on multi-view stereo tracking and artist-enhanced animations. With 5--10 minutes of captured footage, we train a convolutional neural network to produce high-quality output, including self-occluded regions, from a monocular video sequence of that subject. Since this 3D facial performance capture is fully automated, our system can drastically reduce the amount of labor involved in the development of modern narrative-driven video games or films involving realistic digital doubles of actors and potentially hours of animated dialogue per character. We compare our results with several state-of-the-art monocular real-time facial capture techniques and demonstrate compelling animation inference in challenging areas such as eyes and lips. Human pose estimation from monocular video streams is a challenging problem. Much of the work on this problem has focused on developing inference algorithms and probabilistic prior models based on learned measurements. Such algorithms face challenges in generalization beyond the learned dataset. We propose an interactive model-based generative approach for estimating the human pose in 2D from uncalibrated monocular video in unconstrained sports TV footage without any prior learning on motion captured or annotated data. Belief-propagation over a spatio-temporal graph of candidate body part hypotheses is used to estimate a temporally consistent pose between key-frame constraints. Experimental results show that the proposed generative pose estimation framework is capable of estimating pose even in very challenging unconstrained scenarios.