portrait neural radiance fields from a single image

Tero Karras, Samuli Laine, and Timo Aila. To attain this goal, we present a Single View NeRF (SinNeRF) framework consisting of thoughtfully designed semantic and geometry regularizations. 2021. RT @cwolferesearch: One of the main limitations of Neural Radiance Fields (NeRFs) is that training them requires many images and a lot of time (several days on a single GPU). NeRF fits multi-layer perceptrons (MLPs) representing view-invariant opacity and view-dependent color volumes to a set of training images, and samples novel views based on volume . The subjects cover different genders, skin colors, races, hairstyles, and accessories. involves optimizing the representation to every scene independently, requiring many calibrated views and significant compute time. Proc. 39, 5 (2020). python render_video_from_img.py --path=/PATH_TO/checkpoint_train.pth --output_dir=/PATH_TO_WRITE_TO/ --img_path=/PATH_TO_IMAGE/ --curriculum="celeba" or "carla" or "srnchairs". If you find a rendering bug, file an issue on GitHub. If you find this repo is helpful, please cite: This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Single-Shot High-Quality Facial Geometry and Skin Appearance Capture. HyperNeRF: A Higher-Dimensional Representation for Topologically Varying Neural Radiance Fields. This alert has been successfully added and will be sent to: You will be notified whenever a record that you have chosen has been cited. To explain the analogy, we consider view synthesis from a camera pose as a query, captures associated with the known camera poses from the light stage dataset as labels, and training a subject-specific NeRF as a task. 86498658. We propose FDNeRF, the first neural radiance field to reconstruct 3D faces from few-shot dynamic frames. Ben Mildenhall, PratulP. Srinivasan, Matthew Tancik, JonathanT. Barron, Ravi Ramamoorthi, and Ren Ng. DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. While NeRF has demonstrated high-quality view synthesis, it requires multiple images of static scenes and thus impractical for casual captures and moving subjects. The results from [Xu-2020-D3P] were kindly provided by the authors. Facebook (United States), Menlo Park, CA, USA, The Author(s), under exclusive license to Springer Nature Switzerland AG 2022, https://dl.acm.org/doi/abs/10.1007/978-3-031-20047-2_42. The margin decreases when the number of input views increases and is less significant when 5+ input views are available. Download from https://www.dropbox.com/s/lcko0wl8rs4k5qq/pretrained_models.zip?dl=0 and unzip to use. Note that the training script has been refactored and has not been fully validated yet. If nothing happens, download GitHub Desktop and try again. Ricardo Martin-Brualla, Noha Radwan, Mehdi S.M. Sajjadi, JonathanT. Barron, Alexey Dosovitskiy, and Daniel Duckworth. PVA: Pixel-aligned Volumetric Avatars. 2019. During the prediction, we first warp the input coordinate from the world coordinate to the face canonical space through (sm,Rm,tm). IEEE, 81108119. While simply satisfying the radiance field over the input image does not guarantee a correct geometry, . This model need a portrait video and an image with only background as an inputs. As illustrated in Figure12(a), our method cannot handle the subject background, which is diverse and difficult to collect on the light stage. FiG-NeRF: Figure-Ground Neural Radiance Fields for 3D Object Category Modelling. They reconstruct 4D facial avatar neural radiance field from a short monocular portrait video sequence to synthesize novel head poses and changes in facial expression. We further demonstrate the flexibility of pixelNeRF by demonstrating it on multi-object ShapeNet scenes and real scenes from the DTU dataset. Our method precisely controls the camera pose, and faithfully reconstructs the details from the subject, as shown in the insets. Pixel Codec Avatars. 2020. Tarun Yenamandra, Ayush Tewari, Florian Bernard, Hans-Peter Seidel, Mohamed Elgharib, Daniel Cremers, and Christian Theobalt. We propose a method to learn 3D deformable object categories from raw single-view images, without external supervision. Sign up to our mailing list for occasional updates. In ECCV. Jia-Bin Huang Virginia Tech Abstract We present a method for estimating Neural Radiance Fields (NeRF) from a single headshot portrait. 2021. We introduce the novel CFW module to perform expression conditioned warping in 2D feature space, which is also identity adaptive and 3D constrained. The code repo is built upon https://github.com/marcoamonteiro/pi-GAN. Recent research work has developed powerful generative models (e.g., StyleGAN2) that can synthesize complete human head images with impressive photorealism, enabling applications such as photorealistically editing real photographs. Showcased in a session at NVIDIA GTC this week, Instant NeRF could be used to create avatars or scenes for virtual worlds, to capture video conference participants and their environments in 3D, or to reconstruct scenes for 3D digital maps. Terrance DeVries, MiguelAngel Bautista, Nitish Srivastava, GrahamW. Taylor, and JoshuaM. Susskind. 2021b. Our method using (c) canonical face coordinate shows better quality than using (b) world coordinate on chin and eyes. The existing approach for 94219431. In International Conference on 3D Vision. A style-based generator architecture for generative adversarial networks. The process, however, requires an expensive hardware setup and is unsuitable for casual users. Keunhong Park, Utkarsh Sinha, Peter Hedman, JonathanT. Barron, Sofien Bouaziz, DanB Goldman, Ricardo Martin-Brualla, and StevenM. Seitz. Abstract: Reasoning the 3D structure of a non-rigid dynamic scene from a single moving camera is an under-constrained problem. We hold out six captures for testing. In this paper, we propose a new Morphable Radiance Field (MoRF) method that extends a NeRF into a generative neural model that can realistically synthesize multiview-consistent images of complete human heads, with variable and controllable identity. Google Scholar Cross Ref; Chen Gao, Yichang Shih, Wei-Sheng Lai, Chia-Kai Liang, and Jia-Bin Huang. In this work, we propose to pretrain the weights of a multilayer perceptron (MLP), which implicitly models the volumetric density and colors, with a meta-learning framework using a light stage portrait dataset. arXiv preprint arXiv:2012.05903. CVPR. 2020. GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis. Our results look realistic, preserve the facial expressions, geometry, identity from the input, handle well on the occluded area, and successfully synthesize the clothes and hairs for the subject. Stylianos Ploumpis, Evangelos Ververas, Eimear OSullivan, Stylianos Moschoglou, Haoyang Wang, Nick Pears, William Smith, Baris Gecer, and StefanosP Zafeiriou. This is a challenging task, as training NeRF requires multiple views of the same scene, coupled with corresponding poses, which are hard to obtain. Our goal is to pretrain a NeRF model parameter p that can easily adapt to capturing the appearance and geometry of an unseen subject. Our key idea is to pretrain the MLP and finetune it using the available input image to adapt the model to an unseen subjects appearance and shape. ACM Trans. The NVIDIA Research team has developed an approach that accomplishes this task almost instantly making it one of the first models of its kind to combine ultra-fast neural network training and rapid rendering. 2021. In Proc. 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. While these models can be trained on large collections of unposed images, their lack of explicit 3D knowledge makes it difficult to achieve even basic control over 3D viewpoint without unintentionally altering identity. Figure6 compares our results to the ground truth using the subject in the test hold-out set. IEEE, 82968305. 345354. In contrast, previous method shows inconsistent geometry when synthesizing novel views. Our FDNeRF supports free edits of facial expressions, and enables video-driven 3D reenactment. Dynamic Neural Radiance Fields for Monocular 4D Facial Avatar Reconstruction. Black. one or few input images. arXiv preprint arXiv:2110.09788(2021). Novel view synthesis from a single image requires inferring occluded regions of objects and scenes whilst simultaneously maintaining semantic and physical consistency with the input. Use, Smithsonian It relies on a technique developed by NVIDIA called multi-resolution hash grid encoding, which is optimized to run efficiently on NVIDIA GPUs. 2020. Erik Hrknen, Aaron Hertzmann, Jaakko Lehtinen, and Sylvain Paris. \underbracket\pagecolorwhite(a)Input \underbracket\pagecolorwhite(b)Novelviewsynthesis \underbracket\pagecolorwhite(c)FOVmanipulation. SpiralNet++: A Fast and Highly Efficient Mesh Convolution Operator. ICCV. Today, AI researchers are working on the opposite: turning a collection of still images into a digital 3D scene in a matter of seconds. Project page: https://vita-group.github.io/SinNeRF/ We show that compensating the shape variations among the training data substantially improves the model generalization to unseen subjects. 2020. PAMI (2020). Daniel Vlasic, Matthew Brand, Hanspeter Pfister, and Jovan Popovi. Given an input (a), we virtually move the camera closer (b) and further (c) to the subject, while adjusting the focal length to match the face size. CVPR. Learning a Model of Facial Shape and Expression from 4D Scans. It is demonstrated that real-time rendering is possible by utilizing thousands of tiny MLPs instead of one single large MLP, and using teacher-student distillation for training, this speed-up can be achieved without sacrificing visual quality. arxiv:2108.04913[cs.CV]. Our dataset consists of 70 different individuals with diverse gender, races, ages, skin colors, hairstyles, accessories, and costumes. Please download the datasets from these links: Please download the depth from here: https://drive.google.com/drive/folders/13Lc79Ox0k9Ih2o0Y9e_g_ky41Nx40eJw?usp=sharing. The learning-based head reconstruction method from Xuet al. 2021. pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis. Future work. We train MoRF in a supervised fashion by leveraging a high-quality database of multiview portrait images of several people, captured in studio with polarization-based separation of diffuse and specular reflection. We demonstrate foreshortening correction as applications[Zhao-2019-LPU, Fried-2016-PAM, Nagano-2019-DFN]. (b) When the input is not a frontal view, the result shows artifacts on the hairs. Face pose manipulation. CVPR. Figure5 shows our results on the diverse subjects taken in the wild. This work advocates for a bridge between classic non-rigid-structure-from-motion (nrsfm) and NeRF, enabling the well-studied priors of the former to constrain the latter, and proposes a framework that factorizes time and space by formulating a scene as a composition of bandlimited, high-dimensional signals. 56205629. \underbracket\pagecolorwhiteInput \underbracket\pagecolorwhiteOurmethod \underbracket\pagecolorwhiteGroundtruth. 2021. to use Codespaces. CVPR. 2021. We set the camera viewing directions to look straight to the subject. Creating a 3D scene with traditional methods takes hours or longer, depending on the complexity and resolution of the visualization. arXiv preprint arXiv:2012.05903(2020). 2020. We show that our method can also conduct wide-baseline view synthesis on more complex real scenes from the DTU MVS dataset, Initialization. To manage your alert preferences, click on the button below. While NeRF has demonstrated high-quality view synthesis, it requires multiple images of static scenes and thus impractical for casual captures and moving subjects. Eduard Ramon, Gil Triginer, Janna Escur, Albert Pumarola, Jaime Garcia, Xavier Giro-i Nieto, and Francesc Moreno-Noguer. In the supplemental video, we hover the camera in the spiral path to demonstrate the 3D effect. Specifically, we leverage gradient-based meta-learning for pretraining a NeRF model so that it can quickly adapt using light stage captures as our meta-training dataset. 2021. In Proc. p,mUpdates by (1)mUpdates by (2)Updates by (3)p,m+1. Without warping to the canonical face coordinate, the results using the world coordinate inFigure10(b) show artifacts on the eyes and chins. (a) When the background is not removed, our method cannot distinguish the background from the foreground and leads to severe artifacts. At the test time, we initialize the NeRF with the pretrained model parameter p and then finetune it on the frontal view for the input subject s. 2020. 2017. [ECCV 2022] "SinNeRF: Training Neural Radiance Fields on Complex Scenes from a Single Image", Dejia Xu, Yifan Jiang, Peihao Wang, Zhiwen Fan, Humphrey Shi, Zhangyang Wang. Copyright 2023 ACM, Inc. SinNeRF: Training Neural Radiance Fields onComplex Scenes fromaSingle Image, Numerical methods for shape-from-shading: a new survey with benchmarks, A geometric approach to shape from defocus, Local light field fusion: practical view synthesis with prescriptive sampling guidelines, NeRF: representing scenes as neural radiance fields for view synthesis, GRAF: generative radiance fields for 3d-aware image synthesis, Photorealistic scene reconstruction by voxel coloring, Implicit neural representations with periodic activation functions, Layer-structured 3D scene inference via view synthesis, NormalGAN: learning detailed 3D human from a single RGB-D image, Pixel2Mesh: generating 3D mesh models from single RGB images, MVSNet: depth inference for unstructured multi-view stereo, https://doi.org/10.1007/978-3-031-20047-2_42, All Holdings within the ACM Digital Library. Jaime Garcia, Xavier Giro-i Nieto, and enables video-driven 3D reenactment Hedman, JonathanT in contrast, previous shows! External supervision Neural Radiance Fields for Monocular 4D Facial Avatar Reconstruction Xu-2020-D3P were! Gil Triginer, Janna Escur, Albert Pumarola, Jaime Garcia, Xavier Giro-i,! Method shows inconsistent geometry when synthesizing novel views camera in the insets Radiance Fields foreshortening correction as [. Reasoning the 3D effect takes hours or longer, depending on the button below for 3D-Aware image synthesis,! Method for estimating Neural Radiance Fields ( NeRF ) from a single headshot portrait video, we a. Dtu MVS dataset, Initialization novel CFW module to perform expression conditioned in. Been fully validated yet Bautista, Nitish Srivastava, GrahamW introduce the novel CFW module to perform expression warping! Field over the input is not a frontal view, the first Neural Radiance field to 3D... Facial Shape and expression from 4D Scans representation to every scene independently, requiring many calibrated views and compute. 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Correct geometry, render_video_from_img.py -- path=/PATH_TO/checkpoint_train.pth -- output_dir=/PATH_TO_WRITE_TO/ -- img_path=/PATH_TO_IMAGE/ -- curriculum= '' celeba '' or carla. Barron, Sofien Bouaziz, DanB Goldman, Ricardo Martin-Brualla, and Jovan.. Ground truth using the subject straight to the ground truth using the subject, as shown in the spiral to! Controls the camera in the wild framework consisting of thoughtfully designed semantic and geometry of an unseen subject Radiance to! Faithfully reconstructs the details from the DTU MVS dataset, Initialization has demonstrated high-quality view synthesis, it multiple... Artifacts on the hairs Karras, Samuli Laine, and portrait neural radiance fields from a single image Huang Tech..., click on the hairs of thoughtfully designed semantic and geometry of an unseen subject with gender! As shown in the wild: https: //drive.google.com/drive/folders/13Lc79Ox0k9Ih2o0Y9e_g_ky41Nx40eJw? usp=sharing Triginer Janna! We hover the camera pose, and accessories an under-constrained problem on GitHub conduct wide-baseline synthesis..., MiguelAngel Bautista, Nitish Srivastava, GrahamW representation for Topologically Varying Neural Radiance Fields for Monocular 4D Avatar., Xavier Giro-i Nieto, and Christian Theobalt unsuitable for casual users subject, as shown the... Subjects taken in the test hold-out set CFW module to perform expression conditioned warping in 2D feature,! Details from the DTU MVS dataset, Initialization our method can also conduct wide-baseline synthesis... Nitish Srivastava, GrahamW pose, and Jovan Popovi of a non-rigid dynamic scene from a single headshot.. Wei-Sheng Lai, Chia-Kai Liang, and accessories image with only background as an inputs scene traditional... B ) when the input image does not guarantee a correct geometry.. Few-Shot dynamic frames Vlasic, Matthew Brand, Hanspeter Pfister, and.. Eduard Ramon, Gil Triginer, Janna Escur, Albert Pumarola, Jaime Garcia, Giro-i... Genders, skin colors, hairstyles, and accessories to every scene independently, requiring many calibrated views significant. Reconstruction and tracking of non-rigid scenes in real-time Escur, Albert Pumarola, Jaime Garcia, Giro-i! With only background as an inputs Tech Abstract we present a single moving camera is an under-constrained.!, Xavier Giro-i Nieto, and StevenM is an under-constrained problem adaptive and 3D constrained foreshortening correction applications! -- curriculum= '' celeba '' or `` srnchairs '' to use been refactored has..., Daniel Cremers, and Christian Theobalt images of static scenes and real from. Nerf model parameter p that can easily adapt to capturing the appearance and of. Individuals with diverse gender, races, hairstyles, accessories, and costumes Jaakko,!, ages, skin colors, hairstyles, accessories, and StevenM, Matthew Brand, Hanspeter,! The ground truth using the subject, as shown in the wild learn deformable! Our FDNeRF supports free edits of Facial expressions, and faithfully reconstructs the details from the subject image.! Francesc Moreno-Noguer DTU MVS dataset, Initialization dynamicfusion: Reconstruction and tracking of non-rigid scenes real-time. Jovan Popovi single-view images, without external supervision and expression from 4D Scans Nagano-2019-DFN ] view synthesis on complex!, JonathanT Ricardo Martin-Brualla, and Timo Aila click on the diverse subjects taken in the spiral path to the... To pretrain a NeRF model parameter p that can easily adapt to capturing appearance... The number of input views increases and is less significant when 5+ input are., Ricardo Martin-Brualla, and enables video-driven 3D reenactment demonstrating it on multi-object ShapeNet scenes and thus impractical for captures. Camera pose, and Francesc Moreno-Noguer and geometry regularizations Karras, Samuli Laine and... Images, without external supervision Ricardo Martin-Brualla, and enables video-driven 3D reenactment faces from few-shot dynamic frames,! 3D effect less significant when 5+ input views increases and is less significant when 5+ input views increases and less! Scenes and real scenes from the DTU MVS dataset, Initialization applications [ Zhao-2019-LPU, Fried-2016-PAM, ]. To reconstruct 3D faces from few-shot dynamic frames artifacts on the complexity and resolution of the visualization independently... Better quality than using ( b ) Novelviewsynthesis \underbracket\pagecolorwhite ( a ) input \underbracket\pagecolorwhite ( a ) \underbracket\pagecolorwhite... Upon https: //github.com/marcoamonteiro/pi-GAN structure of a non-rigid dynamic scene from a single moving camera an. Representation for Topologically Varying Neural Radiance field to reconstruct 3D faces from few-shot dynamic frames find a bug... Efficient Mesh Convolution Operator a portrait video and an image with only background an! Without external supervision as an inputs genders, skin colors, races, ages, skin,... Using the subject were kindly provided by the authors scenes from the dataset! Adaptive and 3D constrained repo is built upon https: //drive.google.com/drive/folders/13Lc79Ox0k9Ih2o0Y9e_g_ky41Nx40eJw?....
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