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

StyleVideoGAN: A Temporal Generative Model using a Pretrained StyleGAN

Generative adversarial models (GANs) continue to produce advances in terms of the visual quality of still images, as well as the learning of temporal correlations. However, few works manage to combine these two interesting capabilities for the synthesis of video content: Most methods require an extensive training dataset in order to learn temporal correlations, while being rather limited in the resolution and visual quality of their output frames. In this paper, we present a novel approach to the video synthesis problem that helps to greatly improve visual quality and drastically reduce the amount of training data and resources necessary for generating video content. Our formulation separates the spatial domain, in which individual frames are synthesized, from the temporal domain, in which motion is generated. For the spatial domain we make use of a pre-trained StyleGAN network, the latent space of which allows control over the appearance of the objects it was trained for. The expressive power of this model allows us to embed our training videos in the StyleGAN latent space. Our temporal architecture is then trained not on sequences of RGB frames, but on sequences of StyleGAN latent codes. The advantageous properties of the StyleGAN space simplify the discovery of temporal correlations. We demonstrate that it suffices to train our temporal architecture on only 10 minutes of footage of 1 subject for about 6 hours. After training, our model can not only generate new portrait videos for the training subject, but also for any random subject which can be embedded in the StyleGAN space

Text-based Editing of Talking-head Video

Editing talking-head video to change the speech content or to remove filler words is challenging. We propose a novel method to edit talking-head video based on its transcript to produce a realistic output video in which the dialogue of the speaker has been modified, while maintaining a seamless audio-visual flow (i.e. no jump cuts). Our method automatically annotates an input talking-head video with phonemes, visemes, 3D face pose and geometry, reflectance, expression and scene illumination per frame. To edit a video, the user has to only edit the transcript, and an optimization strategy then chooses segments of the input corpus as base material. The annotated parameters corresponding to the selected segments are seamlessly stitched together and used to produce an intermediate video representation in which the lower half of the face is rendered with a parametric face model. Finally, a recurrent video generation network transforms this representation to a photorealistic video that matches the edited transcript. We demonstrate a large variety of edits, such as the addition, removal, and alteration of words, as well as convincing language translation and full sentence synthesis

Neural Voice Puppetry: Audio-driven Facial Reenactment

We present Neural Voice Puppetry, a novel approach for audio-driven facial video synthesis. Given an audio sequence of a source person or digital assistant, we generate a photo-realistic output video of a target person that is in sync with the audio of the source input. This audio-driven facial reenactment is driven by a deep neural network that employs a latent 3D face model space. Through the underlying 3D representation, the model inherently learns temporal stability while we leverage neural rendering to generate photo-realistic output frames. Our approach generalizes across different people, allowing us to synthesize videos of a target actor with the voice of any unknown source actor or even synthetic voices that can be generated utilizing standard text-to-speech approaches. Neural Voice Puppetry has a variety of use-cases, including audio-driven video avatars, video dubbing, and text-driven video synthesis of a talking head. We demonstrate the capabilities of our method in a series of audio- and text-based puppetry examples. Our method is not only more general than existing works since we are generic to the input person, but we also show superior visual and lip sync quality compared to photo-realistic audio- and video-driven reenactment techniques

Resistance of a gamma/gamma prime - delta directionally solidified eutectic alloy to recrystallization

The lamellar directionally solidified nickel-base eutectic alloy gamma/gamma prime-delta has potential as an advanced turbine blade material. The microstructural stability of this alloy was investigated. Specimens were plastically deformed by uniform compression or Brinell indentation, then annealed between 705 and 1120 C. Microstructural changes observed after annealing included gamma prime coarsening, pinch-off and spheroidization of delta lamellae, and the appearance of an unidentified blocky phase in surface layers. All but the first of these was localized in severely deformed regions, suggesting that microstructural instability is not a serious problem in the use of this alloy

Low energy physical properties of high-Tc superconducting Cu oxides: A comparison between the resonating valence bond and experiments

In a recent review by Anderson and coworkers\cite{Vanilla}, it was pointed out that an early resonating valence bond (RVB) theory is able to explain a number of unusual properties of high temperature superconducting (SC) Cu-oxides. Here we extend previous calculations \cite{anderson87,FC Zhang,Randeria} to study more systematically low energy physical properties of the plain vanilla d-wave RVB state, and to compare results with the available experiments. We use a renormalized mean field theory combined with variational Monte Carlo and power Lanczos methods to study the RVB state of an extended $t-J$ model in a square lattice with parameters suitable for the hole doped Cu-oxides. The physical observable quantities we study include the specific heat, the linear residual thermal conductivity, the in-plane magnetic penetration depth, the quasiparticle energy at the antinode $(\pi, 0)$, the superconducting energy gap, the quasiparticle spectra and the Drude weight. The traits of nodes (including $k_{F}$, the Fermi velocity $v_{F}$ and the velocity along Fermi surface $v_{2}$), as well as the SC order parameter are also studied. Comparisons of the theory and the experiments in cuprates show an overall qualitative agreement, especially on their doping dependences.Comment: 12 pages, 14 figures, 1 tabl

Tunable nonlinearity in atomic response to a bichromatic field

Atomic response to a probe beam can be tailored, by creating coherences between atomic levels with help of another beam. Changing parameters of the control beam will change the nature of coherences and hence the nature of atomic response as well. Such change can depend upon intensity of both probe and control beams, in a nonlinear fashion. We present a situation where this nonlinearity in dependence can be precisely controlled, as to obtain different variations as desired. We also present a detailed analysis of how this nonlinear dependency arises and show that this is an interesting effect of several Coherent Population Trap(CPT) states that exist and a competition among them to trap atomic population in them.Comment: 16 pages and 6 figure

gCoRF: Generative Compositional Radiance Fields

3D generative models of objects enable photorealistic image synthesis with 3Dcontrol. Existing methods model the scene as a global scene representation,ignoring the compositional aspect of the scene. Compositional reasoning canenable a wide variety of editing applications, in addition to enablinggeneralizable 3D reasoning. In this paper, we present a compositionalgenerative model, where each semantic part of the object is represented as anindependent 3D representation learned from only in-the-wild 2D data. We startwith a global generative model (GAN) and learn to decompose it into differentsemantic parts using supervision from 2D segmentation masks. We then learn tocomposite independently sampled parts in order to create coherent globalscenes. Different parts can be independently sampled while keeping the rest ofthe object fixed. We evaluate our method on a wide variety of objects and partsand demonstrate editing applications.<br

Underground Cordon by Microorganisms-Part-III Role of Soil Inhabiting Actinomycetes

Certain strains of soil inhabiting actinomycetes were found to substantially corrode aluminium alloy (54-S) which has bscn found tobe more resistant to bacterial or fungal corrosion in our earlier studies.These strains did not produce any corrosion on the mild steel and galvanised iron panels which were heavily corroded by bacteria and fungi. The corrosive isolates have been partialiy characterised after their isolation and purification. The extent of corrosion caused by eachstrain has been determined