We propose Scene Representation Networks (SRNs), a continuous, 3D-structure-aware scene representation that encodes both geometry and appearance. Emerging neural scene representations can be trained only with posed 2D images, but existing methods ignore the three-dimensional structure of scenes. While geometric deep learning has explored 3D-structure-aware representations of scene geometry, these models typically require explicit 3D supervision. We show that the use of additional domain-specific knowledge improves forgery detection to unprecedented accuracy, even in the presence of strong compression, and clearly outperforms human observers.read more read lessĪbstract: Unsupervised learning with generative models has the potential of discovering rich representations of 3D scenes. Based on this data, we performed a thorough analysis of data-driven forgery detectors. This dataset is over an order of magnitude larger than comparable, publicly available, forgery datasets. The benchmark is publicly available and contains a hidden test set as well as a database of over 1.8 million manipulated images. In particular, the benchmark is based on Deep-Fakes, Face2Face, FaceSwap and NeuralTextures as prominent representatives for facial manipulations at random compression level and size. To standardize the evaluation of detection methods, we propose an automated benchmark for facial manipulation detection. This paper examines the realism of state-of-the-art image manipulations, and how difficult it is to detect them, either automatically or by humans. At best, this leads to a loss of trust in digital content, but could potentially cause further harm by spreading false information or fake news. We demonstrate our method in a live setup, where Youtube videos are reenacted in real time.read more read lessĪbstract: The rapid progress in synthetic image generation and manipulation has now come to a point where it raises significant concerns for the implications towards society. Finally, we convincingly re-render the synthesized target face on top of the corresponding video stream such that it seamlessly blends with the real-world illumination. The mouth interior that best matches the re-targeted expression is retrieved from the target sequence and warped to produce an accurate fit. Reenactment is then achieved by fast and efficient deformation transfer between source and target. At run time, we track facial expressions of both source and target video using a dense photometric consistency measure. To this end, we first address the under-constrained problem of facial identity recovery from monocular video by non-rigid model-based bundling. Our goal is to animate the facial expressions of the target video by a source actor and re-render the manipulated output video in a photo-realistic fashion. The source sequence is also a monocular video stream, captured live with a commodity webcam. Abstract: We present a novel approach for real-time facial reenactment of a monocular target video sequence (e.g., Youtube video).

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