Most Neural Radiance Fields (NeRFs) exhibit limited generalization capabilities, which restrict their applicability in representing multiple scenes using a single model. To address this problem, existing generalizable NeRF methods simply condition the model on image features. These methods still struggle to learn precise global representations over diverse scenes since they lack an effective mechanism for interacting among different points and views. In this work, we unveil that 3D implicit representation learning can be significantly improved by mask-based modeling. Specifically, we propose masked ray and view modeling for generalizable NeRF (MRVM-NeRF), which is a self-supervised pretraining target to predict complete scene representations from partially masked features along each ray. With this pretraining target, MRVM-NeRF enables better use of correlations across different points and views as the geometry priors, which thereby strengthens the capability of capturing intricate details within the scenes and boosts the generalization capability across different scenes. Extensive experiments demonstrate the effectiveness of our proposed MRVM-NeRF on both synthetic and real-world datasets, qualitatively and quantitatively. Besides, we also conduct experiments to show the compatibility of our proposed method with various backbones and its superiority under few-shot cases.
Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；University of Tübingen（蒂宾根大学）；ETAS GmbH（埃塔斯有限责任公司）；ETH Zurich（苏黎世联邦理工学院）
Neural implicit 3D representations have emerged as a powerful paradigm for reconstructing surfaces from multi-view images and synthesizing novel views. Unfortunately, existing methods such as DVR or IDR require accurate per-pixel object masks as supervision. At the same time, neural radiance fields have revolutionized novel view synthesis. However, NeRF's estimated volume density does not admit accurate surface reconstruction. Our key insight is that implicit surface models and radiance fields can be formulated in a unified way, enabling both surface and volume rendering using the same model. …
Neural radiance fields (NeRF) have demonstrated the potential of coordinate-based neural representation (neural fields or implicit neural representation) in neural rendering. However, using a multi-layer perceptron (MLP) to represent a 3D scene or object requires enormous computational resources and time. There have been recent studies on how to reduce these computational inefficiencies by using additional data structures, such as grids or trees. Despite the promising performance, the explicit data structure necessitates a substantial amount of memory. In this work, we present a method to redu…
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Tencent AI Lab（腾讯人工智能实验室）；Tsinghua University（清华大学）
Previous portrait image generation methods roughly fall into two categories: 2D GANs and 3D-aware GANs. 2D GANs can generate high fidelity portraits but with low view consistency. 3D-aware GAN methods can maintain view consistency but their generated images are not locally editable. To overcome these limitations, we propose FENeRF, a 3D-aware generator that can produce view-consistent and locally-editable portrait images. Our method uses two decoupled latent codes to generate corresponding facial semantics and texture in a spatial aligned 3D volume with shared geometry. Benefiting from such un…
NeRF-MIR: Towards High-Quality Restoration of Masked Images with Neural Radiance Fields
Shanghai Key Lab of Intelligent Information Processing（上海智能信息处理重点实验室）；College of Computer Science and Artificial Intelligence, Fudan University（复旦大学计算机科学与人工智能学院）；School of Computer Science and Technology, Tongji University（同济大学计算机科学与技术学院）
Facial 3D Morphable Models are a main computer vision subject with countless applications and have been highly optimized in the last two decades. The tremendous improvements of deep generative networks have created various possibilities for improving such models and have attracted wide interest. Moreover, the recent advances in neural radiance fields, are revolutionising novel-view synthesis of known scenes. In this work, we present a facial 3D Morphable Model, which exploits both of the above, and can accurately model a subject's identity, pose and expression and render it in arbitrary illumi…
Reasoning the 3D structure of a non-rigid dynamic scene from a single moving camera is an under-constrained problem. Inspired by the remarkable progress of neural radiance fields (NeRFs) in photo-realistic novel view synthesis of static scenes, extensions have been proposed for dynamic settings. These methods heavily rely on neural priors in order to regularize the problem. In this work, we take a step back and reinvestigate how current implementations may entail deleterious effects, including limited expressiveness, entanglement of light and density fields, and sub-optimal motion localization…
HKUST（香港科技大学）；UC San Diego（加州大学圣地亚哥分校）；Wuhan University（武汉大学）；Kuaishou Technology（快手科技）
We present radiance field propagation (RFP), a novel approach to segmenting objects in 3D during reconstruction given only unlabeled multi-view images of a scene. RFP is derived from emerging neural radiance field-based techniques, which jointly encodes semantics with appearance and geometry. The core of our method is a novel propagation strategy for individual objects' radiance fields with a bidirectional photometric loss, enabling an unsupervised partitioning of a scene into salient or meaningful regions corresponding to different object instances. To better handle complex scenes with multip…
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Beijing Jiaotong University（北京交通大学）；University of Chinese Academy of Sciences（中国科学院大学）；City University of Hong Kong（香港城市大学）
Recent methods for synthesizing 3D-aware face images have achieved rapid development thanks to neural radiance fields, allowing for high quality and fast inference speed. However, existing solutions for editing facial geometry and appearance independently usually require retraining and are not optimized for the recent work of generation, thus tending to lag behind the generation process. To address these issues, we introduce NeRFFaceEditing, which enables editing and decoupling geometry and appearance in the pretrained tri-plane-based neural radiance field while retaining its high quality and …
This paper addresses the challenge of reconstructing an animatable human model from a multi-view video. Some recent works have proposed to decompose a non-rigidly deforming scene into a canonical neural radiance field and a set of deformation fields that map observation-space points to the canonical space, thereby enabling them to learn the dynamic scene from images. However, they represent the deformation field as translational vector field or SE(3) field, which makes the optimization highly under-constrained. Moreover, these representations cannot be explicitly controlled by input motions. I…
Niantic（Niantic公司）；ETH Zurich（苏黎世联邦理工学院）；University College London（伦敦大学学院）
Neural Radiance Fields (NeRFs) are emerging as a ubiquitous scene representation that allows for novel view synthesis. Increasingly, NeRFs will be shareable with other people. Before sharing a NeRF, though, it might be desirable to remove personal information or unsightly objects. Such removal is not easily achieved with the current NeRF editing frameworks. We propose a framework to remove objects from a NeRF representation created from an RGB-D sequence. Our NeRF inpainting method leverages recent work in 2D image inpainting and is guided by a user-provided mask. Our algorithm is underpinned …
Dynamic Neural Radiance Field (NeRF) is a powerful algorithm capable of rendering photo-realistic novel view images from a monocular RGB video of a dynamic scene. Although it warps moving points across frames from the observation spaces to a common canonical space for rendering, dynamic NeRF does not model the change of the reflected color during the warping. As a result, this approach often fails drastically on challenging specular objects in motion. We address this limitation by reformulating the neural radiance field function to be conditioned on surface position and orientation in the obse…
CeRF: Convolutional Neural Radiance Fields for New View Synthesis with Derivatives of Ray Modeling
School of Computer Science and Technology, East China Normal University（华东师范大学计算机科学与技术学院）；School of Data Science and Engineering, East China Normal University（华东师范大学数据科学与工程学院）
Samsung AI Centre Toronto（多伦多三星人工智能中心）；University of Toronto（多伦多大学）；York University（约克大学）；Vector Institute for AI（向量人工智能研究所）
Neural Radiance Fields (NeRFs) have emerged as a popular approach for novel view synthesis. While NeRFs are quickly being adapted for a wider set of applications, intuitively editing NeRF scenes is still an open challenge. One important editing task is the removal of unwanted objects from a 3D scene, such that the replaced region is visually plausible and consistent with its context. We refer to this task as 3D inpainting. In 3D, solutions must be both consistent across multiple views and geometrically valid. In this paper, we propose a novel 3D inpainting method that addresses these challenge…
We propose X-NeRF, a novel method to learn a Cross-Spectral scene representation given images captured from cameras with different light spectrum sensitivity, based on the Neural Radiance Fields formulation. X-NeRF optimizes camera poses across spectra during training and exploits Normalized Cross-Device Coordinates (NXDC) to render images of different modalities from arbitrary viewpoints, which are aligned and at the same resolution. Experiments on 16 forward-facing scenes, featuring color, multi-spectral and infrared images, confirm the effectiveness of X-NeRF at modeling Cross-Spectral scen…
School of Computer Science, Australian Artificial Intelligence Institute (AAII), University of Technology Sydney（悉尼科技大学计算机科学学院、澳大利亚人工智能研究所）；School of Automobile, Chang’an University（长安大学汽车学院）
Institut de Robòtica i Informàtica Industrial, CSIC-UPC（工业机器人与信息学研究所，西班牙国家研究委员会-加泰罗尼亚理工大学）
Neural Radiance Fields (NeRF) has become a popular framework for learning implicit 3D representations and addressing different tasks such as novel-view synthesis or depth-map estimation. However, in downstream applications where decisions need to be made based on automatic predictions, it is critical to leverage the confidence associated with the model estimations. Whereas uncertainty quantification is a long-standing problem in Machine Learning, it has been largely overlooked in the recent NeRF literature. In this context, we propose Stochastic Neural Radiance Fields (S-NeRF), a generalizatio…
Spike-NeRF: Neural Radiance Field Based On Spike Camera
National Engineering Research Center of Visual Technology (NERCVT), Peking University（北京大学视觉技术国家工程研究中心）；College of Future Technology, Peking University（北京大学未来技术学院）
Rasterizing Wireless Radiance Field via Deformable 2D Gaussian Splatting
School of Information Science and Electronic Engineering, Shanghai Jiao Tong University（信息科学与电子工程学院，上海交通大学）；University of Missouri-Kansas City（密苏里大学-凯斯城分校）；Lenovo Group, Lenovo Research（联想集团，联想研究院）
Neural radiance field (NeRF) attracts attention as a promising approach to reconstructing the 3D scene. As NeRF emerges, subsequent studies have been conducted to model dynamic scenes, which include motions or topological changes. However, most of them use an additional deformation network, slowing down the training and rendering speed. Tensorial radiance field (TensoRF) recently shows its potential for fast, high-quality reconstruction of static scenes with compact model size. In this paper, we present D-TensoRF, a tensorial radiance field for dynamic scenes, enabling novel view synthesis at …
BRDF-NeRF: Neural Radiance Fields with Optical Satellite Images and BRDF Modelling
Université de Paris（巴黎大学）；Institut de Physique du Globe de Paris（巴黎地球物理研究所）；CNRS（法国国家科学研究中心）；Université de Gustave Eiffel（古斯塔夫·埃菲尔大学）；IGN-ENSG（法国国家地理信息与林业学院）
HUST（华中科技大学）；Huawei Inc.（华为公司）；Technical University of Munich（慕尼黑工业大学）
Neural radiance fields (NeRF) have shown great success in modeling 3D scenes and synthesizing novel-view images. However, most previous NeRF methods take much time to optimize one single scene. Explicit data structures, e.g. voxel features, show great potential to accelerate the training process. However, voxel features face two big challenges to be applied to dynamic scenes, i.e. modeling temporal information and capturing different scales of point motions. We propose a radiance field framework by representing scenes with time-aware voxel features, named as TiNeuVox. A tiny coordinate deforma…
FruitNeRF: A Unified Neural Radiance Field based Fruit Counting Framework
Friedrich-Alexander-Universität Erlangen-Nürnberg-Fürth（弗里德里希-亚历山大-埃尔兰根-纽伦堡-菲尔特大学）；Fraunhofer Institute for Integrated Circuits (IIS) - EZRT（弗劳恩霍夫集成电路研究所（IIS）- EZRT）；University of Bamberg（班贝格大学）
Peking University（北京大学）；The University of Sydney（悉尼大学）；Baidu Inc.（百度公司）
While dynamic Neural Radiance Fields (NeRF) have shown success in high-fidelity 3D modeling of talking portraits, the slow training and inference speed severely obstruct their potential usage. In this paper, we propose an efficient NeRF-based framework that enables real-time synthesizing of talking portraits and faster convergence by leveraging the recent success of grid-based NeRF. Our key insight is to decompose the inherently high-dimensional talking portrait representation into three low-dimensional feature grids. Specifically, a Decomposed Audio-spatial Encoding Module models the dynamic …
Multispectral-NeRF:a multispectral modeling approach based on neural radiance fields
State Key Laboratory of Climate System Prediction and Risk Management, Nanjing Normal University（南京师范大学气候系统预测与风险管理国家重点实验室）；Key Lab of Virtual Geographic Environment (MOE), Nanjing Normal University（南京师范大学虚拟地理环境教育部重点实验室）；State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province)（地理环境演化国家重点实验室培育基地（江苏省））；Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application（江苏省地理信息资源开发与利用协同创新中心）
TivNe-SLAM: Dynamic Mapping and Tracking via Time-Varying Neural Radiance Fields
School of Information Science and Engineering, Yunnan University（云南大学信息科学与工程学院）；Yunnan Key Laboratory of Intelligent Systems and Computing, Yunnan University（云南大学云南省智能系统与计算重点实验室）
Tsinghua University（清华大学）；Beijing National Research Center for Information Science and Technology（北京信息科学与技术国家研究中心）；PhiGent Robotics（鉴智机器人）
Talking head synthesis is an emerging technology with wide applications in film dubbing, virtual avatars and online education. Recent NeRF-based methods generate more natural talking videos, as they better capture the 3D structural information of faces. However, a specific model needs to be trained for each identity with a large dataset. In this paper, we propose Dynamic Facial Radiance Fields (DFRF) for few-shot talking head synthesis, which can rapidly generalize to an unseen identity with few training data. Different from the existing NeRF-based methods which directly encode the 3D geometry…
