Aditya Agarwal, supervised by Prof. C V Jawahar   received his Master of Science in Computer Science and Engineering (CSE). Here’s a summary of his research work on Revisiting Synthetic Face Generation for Multimedia Applications:

Videos have become an integral part of our daily digital consumption. With the widespread adoption of mobile devices, internet connectivity, and social media platforms, the number of online users and consumers has risen exponentially in recent years. This has led to an unprecedented surge in video content consumption and creation, ranging from short-form content on TikTok to educational material on Coursera and entertainment videos on YouTube. Consequently, there is an urgent need to study videos as a modality in Computer Vision, as it can enable a multitude of applications across various domains, including virtual reality, education, and entertainment. By understanding the intricacies of video content, we can unlock its potential and leverage its benefits to enhance user experiences and create innovative solutions. Producing video content at scale can be challenging due to various practical issues. The recording process can take several hours of practice, and setting up the right studio and camera equipment can be time-consuming and expensive. Moreover, recording requires manual effort, and any mistakes made during the shoot can be difficult to rectify or modify, often requiring the entire video to be re-shot. In this thesis, we aim to ask the question “Can synthetically generated videos take the place of real videos?” as automatic content creation can significantly scale digital media production and ease the process of content creation that can aid several applications. A form of human-centric representation that is becoming increasingly popular in the research community is the ability to generate talking-head videos automatically. Talking-head generation refers to the ability to generate realistic videos of a person speaking, where the generated video can be of a person that may not exist in reality or may exhibit significantly different characteristics than the original person. Recent deep learning approaches can synthesise synthetic talking-head videos at tremendous scale and quality, with diverse content and styles, that are visually indistinguishable from real videos. Therefore, it is imperative to study the process of generating talking-head videos as these videos can be used for a variety of applications, such as video conferencing, movie-making, broadcasting news, vlogging, and language learning among others. Consider a digital avatar reading news from a text transcript being broadcasted on news. In this vein, our thesis aims to explore two prominent use cases of generating synthetic talking-heads automatically – the first one towards generating large-scale synthetic content to aid people in lipreading at scale. The second use case is for automating the task of actor-double face-swapping in the moviemaking industry. We study and elucidate the challenges and limitations of the existing approaches, propose solutions based on synthetic talking head generation, and show the superiority of our methods through extensive experimental evaluation and user studies. In the first task, we address the challenges associated with learning to lipread. Lipreading is a primary mode of communication for people suffering from some form of hearing loss. Therefore, learning to lipread is an important aspect for hard-of-hearing people. However, learning to lipread is not an easy task and finding resources to improve one’s lipreading skills can be challenging. Existing lipreading training websites that provide basic online resources to improve lipreading skills, are unfortunately, limited by real-world variations in the talking faces, cover only a limited vocabulary, and are available in a few select languages and accents. This leaves the vast majority of users without access to adequate lipreading training resources. To address this challenge, we propose an end-to-end pipeline to develop an online lipreading training platform using state-of-the-art talking head video generator networks, text-to-speech models, and computer vision techniques, to increase the amount of online content on the LRT platforms in an automated and cost-effective manner. We design carefully thought out lipreading training exercises, conduct extensive user studies, and perform statistical analysis to show the effectiveness of the generated content in replacing the manually recorded lipreading training videos. In the second problem, we address challenges in the entertainment industry. Body doubles play an indispensable role in the moviemaking industry. They take the place of actors in dangerous stunt scenes and in scenes where the same actor plays multiple characters. In all these scenes, the double’s face is later replaced by the actor’s face and expressions using CGI technology requiring hundreds of hours of manual multimedia edits on heavy graphical units costing millions of dollars and taking months to complete. As we show in this thesis, automated face-swapping approaches based on deep learning models are not suitable for the task of actor-double face-swapping, as they fail to preserve the actor’s expressions. To address this, we introduce “video-to-video (V2V) face-swapping”, a novel task of face-swapping that aims to (1) swap the identity and expressions of a source face video, and (2) retain the pose and background of the target face video. We propose a self-supervised training strategy using temporal autoencoding models that can take two different motions as input and produce a third coherent output motion. In summary, this thesis unravels several tasks enabled by synthetic talking-head generation, and provides solutions for the lipreading community and the moviemaking industry. Our findings concretely point toward the notion of replacing real human talking-head videos with synthetically generated videos, thereby, scaling digital content creation to new heights, saving precious time and resources, and easing the life of humans.

October 2023