We present LumiereNet, a simple, modular, and completely deep-learning based architecture that synthesizes, high quality, full-pose headshot lecture videos from instructor's new audio narration of any length. Unlike prior works, LumiereNet is entirely composed of trainable neural network modules to learn mapping functions from the audio to video through (intermediate) estimated pose-based compact and abstract latent codes. Our video demos are available at [22] and [23].
* Visual speech synthesis Over the last two decades, there has been extensive study dedicated towards creating realistic animations for speech @cite_27 in 2D or 3D. 2D has the advantage that video cutouts of the mouth area can be used and combined leading to realistic visualizations @cite_0 @cite_19 . 3D approaches are much more versatile, as viewpoints and illumination can be changed at will @cite_16 . Given that our goal is to produce 2D animation based on audio, instead of formulating entire mapping as an end-to-end optimization task, we are inherently interested in the intermediate representations. Recent advances in this line were more or less focused on synthesizing only the parts of the face (around the mouth) and borrowing the rest of the subject from existing video footage @cite_9 @cite_8 @cite_24 .
@cite_8 @cite_9 @cite_0 @cite_19 @cite_27 @cite_24 @cite_16
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This paper presents a method for photo-realistic animation of any face shown in a single image or a video. The technique does not require example data of the persons mouth movements, and the image to be animated is not restricted in pose and illumination. Video reanimation allows for head rotations and speech in the original sequence, yet neither of these motions is required. In order to animate novel faces, the system transfers mouth movements and expressions across individuals, based a common representation of different identities and facial expressions in a vector space of 3D shapes and textures. This space is computed from 3D scans of different neutral faces, and scans of facial expressions. The 3D models versatility with respect to pose and illumination is conveyed to photo-realistic image and video processing by a framework of analysis and synthesis algorithms: The system automatically estimates 3D shape, pose and other rendering parameters from single images, and tracks head pose and mouth movements in video. Reanimated with new mouth movements, the 3D face is rendered into the original images. Automatic audio-visual speech recognition currently lags behind its audio-only counterpart in terms of major progress. One of the reasons commonly cited by researchers is the scarcity of suitable research corpora. This paper details the creation of a new corpus designed for continuous audio-visual speech recognition research . TCD-TIMIT consists of high-quality audio and video footage of 62 speakers reading a total of 6913 phonetically rich sentences. Three of the speakers are professionally-trained lipspeakers, recorded to test the hypothesis that lipspeakers may have an advantage over regular speakers in automatic visual speech recognition systems. Video footage was recorded from two angles: straight on, and at @math . The paper outlines the recording of footage, and the required post-processing to yield video and audio clips for each sentence. Audio, visual, and joint audio-visual baseline experiments are reported. Separate experiments were run on the lipspeaker and non-lipspeaker data, and the results compared. Visual and audio-visual baseline results on the non-lipspeakers were low overall. Results on the lipspeakers were found to be significantly higher. It is hoped that as a publicly available database, TCD-TIMIT will now help further state of the art in audio-visual speech recognition research. A citing sentence is one that appears in a scientific article and cites previous work. Citing sentences have been studied and used in many applications. For example, they have been used in scientific paper summarization, automatic survey generation, paraphrase identification, and citation function classification. Citing sentences that cite multiple papers are common in scientific writing. This observation should be taken into consideration when using citing sentences in applications. For instance, when a citing sentence is used in a summary of a scientific paper, only the fragments of the sentence that are relevant to the summarized paper should be included in the summary. In this paper, we present and compare three different approaches for identifying the fragments of a citing sentence that are related to a given target reference. Our methods are: word classification, sequence labeling, and segment classification. Our experiments show that segment classification achieves the best results. In recent years scholars have built maps of science by connecting the academic fields that cite each other, are cited together, or that cite a similar literature. But since scholars cannot always publish in the fields they cite, or that cite them, these science maps are only rough proxies for the potential of a scholar, organization, or country, to enter a new academic field. Here we use a large dataset of scholarly publications disambiguated at the individual level to create a map of science-or research space-where links connect pairs of fields based on the probability that an individual has published in both of them. We find that the research space is a significantly more accurate predictor of the fields that individuals and organizations will enter in the future than citation based science maps. At the country level, however, the research space and citations based science maps are equally accurate. These findings show that data on career trajectories-the set of fields that individuals have previously published in-provide more accurate predictors of future research output for more focalized units-such as individuals or organizations-than citation based science maps. In a document network such as a citation network of scientific documents, web-logs, etc., the content produced by authors exhibits their interest in certain topics. In addition some authors influence other authors' interests. In this work, we propose to model the influence of cited authors along with the interests of citing authors. Moreover, we hypothesize that apart from the citations present in documents, the context surrounding the citation mention provides extra topical information about the cited authors. However, associating terms in the context to the cited authors remains an open problem. We propose novel document generation schemes that incorporate the context while simultaneously modeling the interests of citing authors and influence of the cited authors. Our experiments show significant improvements over baseline models for various evaluation criteria such as link prediction between document and cited author, and quantitatively explaining unseen text. We propose a new 3D photo-realistic talking head with a personalized, photo realistic appearance. Different head motions and facial expressions can be freely controlled and rendered. It extends our prior, high-quality, 2D photo-realistic talking head to 3D. Around 20-minutes of audio-visual 2D video are first recorded with read prompted sentences spoken by a speaker. We use a 2D-to-3D reconstruction algorithm to automatically adapt a general 3D head mesh model to the individual. In training, super feature vectors consisting of 3D geometry, texture and speech are formed to train a statistical, multi-streamed, Hidden Markov Model (HMM). The HMM is then used to synthesize both the trajectories of geometry animation and dynamic texture. The 3D talking head animation can be controlled by the rendered geometric trajectory while the facial expressions and articulator movements are rendered with the dynamic 2D image sequences. Head motions and facial expression can also be separately controlled by manipulating corresponding parameters. The new 3D talking head has many useful applications such as voice-agent, tele-presence, gaming, social networking, etc. Index Terms: audio visual synthesis, 3D, photo-realistic, talking head We describe how to create with machine learning techniques a generative, speech animation module. A human subject is first recorded using a videocamera as he she utters a predetermined speech corpus. After processing the corpus automatically, a visual speech module is learned from the data that is capable of synthesizing the human subject's mouth uttering entirely novel utterances that were not recorded in the original video. The synthesized utterance is re-composited onto a background sequence which contains natural head and eye movement. The final output is videorealistic in the sense that it looks like a video camera recording of the subject. At run time, the input to the system can be either real audio sequences or synthetic audio produced by a text-to-speech system, as long as they have been phonetically aligned.The two key contributions of this paper are 1) a variant of the multidimensional morphable model (MMM) to synthesize new, previously unseen mouth configurations from a small set of mouth image prototypes; and 2) a trajectory synthesis technique based on regularization, which is automatically trained from the recorded video corpus, and which is capable of synthesizing trajectories in MMM space corresponding to any desired utterance.