Face Reenactment: Crafting Realistic Talking Heads for Enhanced Video Communication and Beyond

Madhav Agarwal

Abstract

Face Reenactment and Synthetic Talking Head works have been widely popular for creating realistic face animations by using a single image of a person. In light of the recent developments in processing facial features in images and videos, as well as the ability to create realistic talking heads, We are focusing on two promising applications. These applications include utilizing face reenactment for movie dubbing and compressing video calls where the primary object is a talking face. We propose a novel method to generate realistic talking head videos using audio and visual streams. We animate a source image by transferring head motion from a driving video using a dense motion field generated using learnable keypoints. We use audio as an additional input for high-quality lip sync, by helping the network to attend to the mouth region. We use additional priors using face segmentation and face mesh to preserve the structure of the reconstructed faces. Finally, we incorporate a carefully designed identity- aware generator module to get realistic quality of talking heads. The identity-aware generator takes the source image and the warped motion features as input to generate a high-quality output with fine-grained details. Our method produces state-of-the-art results and generalizes well to unseen faces, languages, and voices. We comprehensively evaluate our approach using multiple metrics and outperforming the current techniques both qualitative and quantitatively. Our work opens up several applications, including enabling low-bandwidth video calls and movie dubbing. We leverage the advancements in talking head generation to propose an end-to-end system for video call compression. Our algorithm transmits pivot frames intermittently while the rest of the talking head video is generated by animating them. We use a state-of-the-art face reenactment network to detect keypoints in the non-pivot frames and transmit them to the receiver. A dense flow is then calculated to warp a pivot frame to reconstruct the non-pivot ones. Transmitting keypoints instead of full frames leads to significant compression. We propose a novel algorithm to adaptively select the best-suited pivot frames at regular intervals to provide a smooth experience. We also propose a frame-interpolater at the receiver’s end to improve the compression levels further. Finally, a face enhancement network improves reconstruction quality, significantly improving several aspects, like the sharpness of the generations. We evaluate our method both qualitatively and quantitatively on benchmark datasets and compare it with multiple compression techniques

Year of completion:  June 2023
 Advisor : C V Jawahar, Vinay P Namboodiri

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    Fingerprint Disentanglement for Presentation Attack Generalization Across Sensors and Materials

    Gowri Lekshmy

    Abstract

    In today's digital era, biometric authentication has become increasingly widespread for verifying a user across a range of applications, from unlocking a smartphone to securing high-end systems. Various biometric modalities such as fingerprint, face, and iris offer a distinct way to recognize a person automatically. Fingerprints are one of the most prevalent biometric modalities. They are widely utilized in security systems owing to their remarkable reliability, distinctiveness, invariance over time and user convenience. Nowadays, automatic fingerprint recognition systems have become a prime target for attackers. Attackers fabricate fingerprints using materials like playdoh and gelatin, making it hard to distinguish them from live fingerprints. This way of circumventing biometric systems is called a presentation attack (PA). To identify such attacks, a PA detector is added to these systems. Deep learning-based PA detectors require large amounts of data to distinguish PA fingerprints from live ones. However, there exists significantly less training data with novel sensors and materials. Due to this, PA detectors do not generalize well on introducing unknown sensors or materials. It is incredibly challenging to physically fabricate an extensive train dataset of high-quality counterfeit fingerprints generated with novel materials captured across multiple sensors. Existing fingerprint presentation attack detection (FPAD) solutions improve cross-sensor and cross-material generalization by utilizing styletransfer-based augmentation wrappers over a two-class PA classifier. These solutions generate large artificial datasets for training by using style transfer which learns the style properties from a few samples obtained from the attacker. They synthesize data by learning the style as a single entity, containing both sensor and material characteristics. However, these strategies necessitate learning the entire style upon adding a new sensor for an already known material or vice versa. This thesis proposes a decomposition-based approach to improve cross-sensor and cross-material FPAD generalization. We model presentation attacks as a combination of two underlying components, i.e., material and sensor, rather than the entire style. By utilizing this approach, our method can generate synthetic patches upon introducing either a new sensor, a new material, or both. We perform two different methods of fingerprint factorization - traditional and deep-learning based. Traditional factorization of fingerprints into sensor and material representations using tensor decomposition establishes a baseline using machine learning for our hypothesis. The deep-learning method uses a decompositionbased augmentation wrapper for disentangling fingerprint style. The wrapper improves cross-sensor and cross-material FPAD, utilizing one fingerprint image of the target sensor and material. We also reduce vi vii computational complexity by generating compact representations and utilizing lesser combinations of sensors and materials to produce several styles. Our approach enables us to generate a large variety of samples using a limited amount of data, which helps improve generalization

    Year of completion:  June 2023
     Advisor : Anoop M Namboodiri

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      Extending PRT Framework for Lowly-Tessellated and Continuous Surfaces

      Dhawal Sirikonda

      Abstract

      Precomputed Radiance Transfer (PRT) is widely used for real-time photorealistic effects. PRT dis entangles the rendering equation into transfer and lighting, enabling their precomputation. Transfer accounts for the cosine-weighted visibility of points in the scene, while Lighting is usually a distant emitted lighting, e.g., environment. Transfer computation involves tracing several rays into the scene from every point on the surface. For every ray, the binary visibility is calculated, and a spherical function is obtained. The spherical function is projected into Spherical Harmonic(SH) domain. SH is a band-limited representation of spherical functions, and the order of SH decides the representation capacity of the SH (the higher the SH order better the approximation of a spherical function). The SH domain also facilitates fast and efficient integral computation by simplifying the integral into simple dot products and convolutions. The original formulation of PRT by Sloan et al. 2002 provides different storage requirements for the transfer—vectors in the case of diffuse materials and matrices in the case of glossy materials. Using matrices for Transfer representation makes it infeasible as the SH orders increase. The work of Triple Product Formulation by Ng et al. in 2004 extended the formulation to allow simple vector-based Transfer storage even for the case of glossy materials. Prior art stored precomputed transfer in a tabulated manner in vertex space. These values are fetched with interpolation at each point for shading. Since the barycentric interpolation is finally employed to calculate the final color across the geometry apart from the vertex locations, the vertex space methods require densely tessellated mesh vertices to obtain accurate radiance. Sometimes high-density(tessellated) meshes adversely affect runtimes and memory requirements. This is mainly observed in simple geometries with no additional detailing but still demanding higher triangle counts (e.g., planes, walls, etc.). The first work provides a solution by leveraging Texture space, which is more continuous than the Vertex space. We also added additional functionality to obtain inter-reflection effects in the texture space. While Texture space methods provide faithful results in meshes, they require non-overlapping, areapreserving UV mapping, and a high-resolution texture to avoid artifacts. In the subsequent work, we propose a compact transfer representation that is learnt directly on scene geometry points. Specifically, we train a small multi-layer perceptron (MLP) to predict the transfer at sampled surface points. Our approach is most beneficial where inherent mesh storage structure and natural UV mapping are unavailable, such as Implicit Surfaces, as it learns the transfer values directly on the surface. Using our approach, we demonstrate real-time, photorealistic renderings of diffuse and glossy materials on SDF geometries with PRT.

      Year of completion:  June 2023
       Advisor : P J Narayanan

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        Virtual World Creation

        Aryamaan Jain

        Abstract

        Synthesis, capture and analysis of a highly complex 3D terrain structure are essential for critical applications such as river/flood modelling, disaster mitigation planning, landslide modelling and flight simulation. On the other hand, synthesis of natural-looking 3D terrains finds its applications in the entertainment industry such as computer gaming and VFX. This thesis explores novel learning-based techniques for the generation of immersive and realistic 3D virtual environments, catering to the needs of the aforementioned applications. The generation of virtual worlds involves multiple components, including terrain, vegetation, and other objects. We primarily focus on three key aspects for virtual world generation: 1) develop novel AI-enabled 3D terrain authoring solutions based on real-world satellite and aerial data using a learning-based framework, 2) L-systems grammar-based 3D tree generation and 3) rendering techniques that are used to create high-quality visualizations of the generated world. Terrain generation is a critical component of 3D virtual world generation, as it provides the foundational structure for the environment. Traditional techniques for 3D terrain generation involve procedural generation, which relies on mathematical algorithms to generate landscapes. However, deep learning techniques have shown promise in generating more realistic terrain, as they can learn from real-world data to produce new, varied, and realistic landscapes. In this thesis, we explore the use of deep learning techniques for 3D terrain generation, which can produce realistic and varied terrains with high visual fidelity. Specifically, we propose two learning-based novel frameworks for Interactive 3D Terrain Authoring & Manipulation and Adaptive Multi-Resolution Infinite Terrain Generation. In addition to terrain generation, vegetation is another important component of virtual world generation. Trees and other plants provide visual interest and can help create a more immersive environment. L-systems are a popular technique for generating realistic vegetation, as they are capable of generating complex structures that resemble real-world plants. In this thesis, we propose a variant of the L-systems for 3D tree generation and compare the results to traditional procedural generation techniques. Finally, rendering is a critical component of 3D virtual world generation, as it is responsible for creating the final visual output that users will see. In addition to terrain and tree generation, this thesis also covers rendering techniques used to visualize the generated virtual world. We explore the use of real-time rendering techniques in conjunction with terrain generation to achieve high-quality visual results while maintaining performance. Overall, the research presented in this thesis aims to advance the state-of-the-art in virtual world generation and contribute to the development of more realistic and immersive virtual environments. We performed extensive empirical evaluation on publicly available datasets to report details qualitative and quantitative results demonstrating the superiority of the proposed methods over existing solutions in the literature.

        Year of completion:  July 2023
         Advisor : Avinash Sharma, Rajan Krishnan Sundara

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          Driving into the Dataverse: Real and Synthetic Data for Autonomous Vehicles

          Shubham Dokania

          Abstract

          The rapid advancement of autonomous driving systems is transforming the future of transportation and urban mobility. However, these systems face significant challenges when deployed in complex and unstructured traffic environments, such as those found in many cities in south-east Asian countries like India. This thesis aims to address the challenges related to data collection, management, and generation for autonomous driving systems operating in such scenarios. The primary contributions of this work include the development of a data collection toolkit, the creation of a comprehensive driving dataset for unstructured environments (IDD-3D), and the proposal of a synthetic data generation framework (TRoVE) based on real-world data. The data collection toolkit presented in this thesis enables sensor fusion for driving scenarios by treating sensor APIs as separate entities from the data collection interface. This framework allows for the use of any sensor configuration and demonstrates the smooth creation of driving datasets using our framework. This toolkit is adaptable to different environments and can be easily scaled, making it a crucial step towards creating a large-scale dataset for Indian road scenarios. The IDD-3D dataset provides a valuable resource for studying unstructured driving scenarios with complex road situations. We present a thorough statistical and experimental analysis of the dataset, which includes high-quality annotations for 3D object bounding boxes and instance IDs for tracking. We highlight the diverse object types, categories, and complex trajectories found in Indian road scenes, enabling the development of robust autonomous driving systems that can generalize across different geographical locations. In addition, we provide benchmarks for 3D object detection and tracking using state-of-the-art approaches. The TRoVE synthetic data toolkit offers a framework for the automatic generation of synthetic data for visual perception, leveraging existing real-world data. By combining synthetic data with real data, we show the potential for improved performance in various computer vision tasks. The data generation process can be extended to different locations and scenarios, avoiding the limitations of bounded data volumes and variety found in manually designed virtual environments. This thesis contributes to the development of systems in complex environments by addressing the challenges of data acquisition, management, and generation. By bridging the gap between the current state-of-the-art and the needs of unstructured traffic scenarios, this work paves the way for more robust and versatile intelligent transportation systems that can operate safely and efficiently in a wide range of situations

          Year of completion:  July 2023
           Advisor : C V Jawahar

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