Real-time rendering of human head avatars is a cornerstone of many computer graphics applications, such as augmented reality, video games, and films, to name a few. Recent approaches address this challenge with computationally efficient geometry primitives in a carefully calibrated multi-view setup. Albeit producing photorealistic head renderings, it often fails to represent complex motion changes such as the mouth interior and strongly varying head poses. We propose a new method to generate highly dynamic and deformable human head avatars from multi-view imagery in real-time. At the core of our method is a hierarchical representation of head models that allows to capture the complex dynamics of facial expressions and head movements. First, with rich facial features extracted from raw input frames, we learn to deform the coarse facial geometry of the template mesh. We then initialize 3D Gaussians on the deformed surface and refine their positions in a fine step. We train this coarse-to-fine facial avatar model along with the head pose as a learnable parameter in an end-to-end framework. This enables not only controllable facial animation via video inputs, but also high-fidelity novel view synthesis of challenging facial expressions, such as tongue deformations and fine-grained teeth structure under large motion changes. Moreover, it encourages the learned head avatar to generalize towards new facial expressions and head poses at inference time. We demonstrate the performance of our method with comparisons against the related methods on different datasets, spanning challenging facial expression sequences across multiple identities. We also show the potential application of our approach by demonstrating a cross-identity facial performance transfer application.
GaussianHead: High-fidelity Head Avatars with Learnable Gaussian Derivation
School of Artificial Intelligence, Nanjing University of Posts and Telecommunications（南京邮电大学人工智能学院）；BNRist, Tsinghua University（清华大学BNRist）；Department of Computer and Information Science, University of Macau（澳门大学计算机与信息科学系）
SMPLX-Lite: A Realistic and Drivable Avatar Benchmark with Rich Geometry and Texture Annotations
Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Prometheus Vision Technology Co., Ltd.（普罗米修斯视觉科技有限公司）；Beijing University of Aeronautics and Astronautics（北京航空航天大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
SEGA: Drivable 3D Gaussian Head Avatar from a Single Image
Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）；ByteDance（字节跳动）；Pengcheng Lab（鹏城实验室）；Department of Automation, Tsinghua University（清华大学自动化系）
Photorealistic telepresence requires both high-fidelity body modeling and faithful driving to enable dynamically synthesized appearance that is indistinguishable from reality. In this work, we propose an end-to-end framework that addresses two core challenges in modeling and driving full-body avatars of real people. One challenge is driving an avatar while staying faithful to details and dynamics that cannot be captured by a global low-dimensional parameterization such as body pose. Our approach supports driving of clothed avatars with wrinkles and motion that a real driving performer exhibits…
Technion – Israel Institute of Technology（以色列理工学院）；Facebook Reality Labs（Facebook现实实验室）；Carnegie Mellon University（卡内基梅隆大学）
Virtual telepresence is the future of online communication. Clothing is an essential part of a person's identity and self-expression. Yet, ground truth data of registered clothes is currently unavailable in the required resolution and accuracy for training telepresence models for realistic cloth animation. Here, we propose an end-to-end pipeline for building drivable representations for clothing. The core of our approach is a multi-view patterned cloth tracking algorithm capable of capturing deformations with high accuracy. We further rely on the high-quality data produced by our tracking meth…
We present HandAvatar, a novel representation for hand animation and rendering, which can generate smoothly compositional geometry and self-occlusion-aware texture. Specifically, we first develop a MANO-HD model as a high-resolution mesh topology to fit personalized hand shapes. Sequentially, we decompose hand geometry into per-bone rigid parts, and then re-compose paired geometry encodings to derive an across-part consistent occupancy field. As for texture modeling, we propose a self-occlusion-aware shading field (SelF). In SelF, drivable anchors are paved on the MANO-HD surface to record alb…
AvatarMakeup: Realistic Makeup Transfer for 3D Animatable Head Avatars
Institute of Information Science and Visual Intelligence + X International Joint Laboratory, Beijing Jiaotong University（信息科学与视觉智能+X联合实验室，北京交通大学）；College of Electrical Engineering and Automation, Shandong University of Science and Technology（电气工程与自动化学院，山东科技大学）；School of Mathematical Sciences, University of Science and Technology of China（数学科学学院，中国科学技术大学）
TEDRA: Text-based Editing of Dynamic and Photoreal Actors
Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；University of Freiburg（弗莱堡大学）；Saarbrücken Research Center for Visual Computing, Interaction and AI（萨尔布吕肯视觉计算、交互与人工智能研究中心）
UV Gaussians: Joint Learning of Mesh Deformation and Gaussian Textures for Human Avatar Modeling
Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Tencent AI Lab（腾讯AI实验室）；The Hong Kong University of Science and Technology（香港科技大学）
Relightable Neural Actor with Intrinsic Decomposition and Pose Control
Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；Saarland Informatics Campus（萨尔兰信息学园区）；Saarbrücken Research Center for Visual Computing, Interaction and AI（萨尔布吕肯视觉计算、交互与人工智能研究中心）；University of Freiburg（弗莱堡大学）
