RIGID: Recurrent GAN Inversion and Editing of Real Face Videos

GAN inversion is indispensable for applying the powerful editability of GAN to real images. However, existing methods invert video frames individually often leading to undesired inconsistent results over time. In this paper, we propose a unified recurrent framework, named \textbf{R}ecurrent v\textbf{I}deo \textbf{G}AN \textbf{I}nversion and e\textbf{D}iting (RIGID), to explicitly and simultaneously enforce temporally coherent GAN inversion and facial editing of real videos. Our approach models the temporal relations between current and previous frames from three aspects. To enable a faithful real video reconstruction, we first maximize the inversion fidelity and consistency by learning a temporal compensated latent code. Second, we observe incoherent noises lie in the high-frequency domain that can be disentangled from the latent space. Third, to remove the inconsistency after attribute manipulation, we propose an \textit{in-between frame composition constraint} such that the arbitrary frame must be a direct composite of its neighboring frames. Our unified framework learns the inherent coherence between input frames in an end-to-end manner, and therefore it is agnostic to a specific attribute and can be applied to arbitrary editing of the same video without re-training. Extensive experiments demonstrate that RIGID outperforms state-of-the-art methods qualitatively and quantitatively in both inversion and editing tasks. The deliverables can be found in \url{https://cnnlstm.github.io/RIGID}Comment: ICCV202

Video-driven Neural Physically-based Facial Asset for Production

Production-level workflows for producing convincing 3D dynamic human faces have long relied on an assortment of labor-intensive tools for geometry and texture generation, motion capture and rigging, and expression synthesis. Recent neural approaches automate individual components but the corresponding latent representations cannot provide artists with explicit controls as in conventional tools. In this paper, we present a new learning-based, video-driven approach for generating dynamic facial geometries with high-quality physically-based assets. For data collection, we construct a hybrid multiview-photometric capture stage, coupling with ultra-fast video cameras to obtain raw 3D facial assets. We then set out to model the facial expression, geometry and physically-based textures using separate VAEs where we impose a global MLP based expression mapping across the latent spaces of respective networks, to preserve characteristics across respective attributes. We also model the delta information as wrinkle maps for the physically-based textures, achieving high-quality 4K dynamic textures. We demonstrate our approach in high-fidelity performer-specific facial capture and cross-identity facial motion retargeting. In addition, our multi-VAE-based neural asset, along with the fast adaptation schemes, can also be deployed to handle in-the-wild videos. Besides, we motivate the utility of our explicit facial disentangling strategy by providing various promising physically-based editing results with high realism. Comprehensive experiments show that our technique provides higher accuracy and visual fidelity than previous video-driven facial reconstruction and animation methods.Comment: For project page, see https://sites.google.com/view/npfa/ Notice: You may not copy, reproduce, distribute, publish, display, perform, modify, create derivative works, transmit, or in any way exploit any such content, nor may you distribute any part of this content over any network, including a local area network, sell or offer it for sale, or use such content to construct any kind of databas

MetaPortrait: Identity-Preserving Talking Head Generation with Fast Personalized Adaptation

In this work, we propose an ID-preserving talking head generation framework, which advances previous methods in two aspects. First, as opposed to interpolating from sparse flow, we claim that dense landmarks are crucial to achieving accurate geometry-aware flow fields. Second, inspired by face-swapping methods, we adaptively fuse the source identity during synthesis, so that the network better preserves the key characteristics of the image portrait. Although the proposed model surpasses prior generation fidelity on established benchmarks, to further make the talking head generation qualified for real usage, personalized fine-tuning is usually needed. However, this process is rather computationally demanding that is unaffordable to standard users. To solve this, we propose a fast adaptation model using a meta-learning approach. The learned model can be adapted to a high-quality personalized model as fast as 30 seconds. Last but not the least, a spatial-temporal enhancement module is proposed to improve the fine details while ensuring temporal coherency. Extensive experiments prove the significant superiority of our approach over the state of the arts in both one-shot and personalized settings.Comment: CVPR 2023, project page: https://meta-portrait.github.i

Photo-realistic face synthesis and reenactment with deep generative models

The advent of Deep Learning has led to numerous breakthroughs in the field of Computer Vision. Over the last decade, a significant amount of research has been undertaken towards designing neural networks for visual data analysis. At the same time, rapid advancements have been made towards the direction of deep generative modeling, especially after the introduction of Generative Adversarial Networks (GANs), which have shown particularly promising results when it comes to synthesising visual data. Since then, considerable attention has been devoted to the problem of photo-realistic human face animation due to its wide range of applications, including image and video editing, virtual assistance, social media, teleconferencing, and augmented reality. The objective of this thesis is to make progress towards generating photo-realistic videos of human faces. To that end, we propose novel generative algorithms that provide explicit control over the facial expression and head pose of synthesised subjects. Despite the major advances in face reenactment and motion transfer, current methods struggle to generate video portraits that are indistinguishable from real data. In this work, we aim to overcome the limitations of existing approaches, by combining concepts from deep generative networks and video-to-video translation with 3D face modelling, and more specifically by capitalising on prior knowledge of faces that is enclosed within statistical models such as 3D Morphable Models (3DMMs). In the first part of this thesis, we introduce a person-specific system that performs full head reenactment using ideas from video-to-video translation. Subsequently, we propose a novel approach to controllable video portrait synthesis, inspired from Implicit Neural Representations (INR). In the second part of the thesis, we focus on person-agnostic methods and present a GAN-based framework that performs video portrait reconstruction, full head reenactment, expression editing, novel pose synthesis and face frontalisation.Open Acces

FaceVR: Real-Time Facial Reenactment and Eye Gaze Control in Virtual Reality

We introduce FaceVR, a novel method for gaze-aware facial reenactment in the Virtual Reality (VR) context. The key component of FaceVR is a robust algorithm to perform real-time facial motion capture of an actor who is wearing a head-mounted display (HMD), as well as a new data-driven approach for eye tracking from monocular videos. In addition to these face reconstruction components, FaceVR incorporates photo-realistic re-rendering in real time, thus allowing artificial modifications of face and eye appearances. For instance, we can alter facial expressions, change gaze directions, or remove the VR goggles in realistic re-renderings. In a live setup with a source and a target actor, we apply these newly-introduced algorithmic components. We assume that the source actor is wearing a VR device, and we capture his facial expressions and eye movement in real-time. For the target video, we mimic a similar tracking process; however, we use the source input to drive the animations of the target video, thus enabling gaze-aware facial reenactment. To render the modified target video on a stereo display, we augment our capture and reconstruction process with stereo data. In the end, FaceVR produces compelling results for a variety of applications, such as gaze-aware facial reenactment, reenactment in virtual reality, removal of VR goggles, and re-targeting of somebody's gaze direction in a video conferencing call

That's What I Said: Fully-Controllable Talking Face Generation

The goal of this paper is to synthesise talking faces with controllable facial motions. To achieve this goal, we propose two key ideas. The first is to establish a canonical space where every face has the same motion patterns but different identities. The second is to navigate a multimodal motion space that only represents motion-related features while eliminating identity information. To disentangle identity and motion, we introduce an orthogonality constraint between the two different latent spaces. From this, our method can generate natural-looking talking faces with fully controllable facial attributes and accurate lip synchronisation. Extensive experiments demonstrate that our method achieves state-of-the-art results in terms of both visual quality and lip-sync score. To the best of our knowledge, we are the first to develop a talking face generation framework that can accurately manifest full target facial motions including lip, head pose, and eye movements in the generated video without any additional supervision beyond RGB video with audio