portrait neural radiance fields from a single image

PlenOctrees for Real-time Rendering of Neural Radiance Fields. 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. Vol. 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. We train a model m optimized for the front view of subject m using the L2 loss between the front view predicted by fm and Ds CVPR. 2021. Our method focuses on headshot portraits and uses an implicit function as the neural representation. 2019. We also thank In Proc. Tero Karras, Samuli Laine, and Timo Aila. To leverage the domain-specific knowledge about faces, we train on a portrait dataset and propose the canonical face coordinates using the 3D face proxy derived by a morphable model. During the prediction, we first warp the input coordinate from the world coordinate to the face canonical space through (sm,Rm,tm). Zixun Yu: from Purdue, on portrait image enhancement (2019) Wei-Shang Lai: from UC Merced, on wide-angle portrait distortion correction (2018) Publications. FLAME-in-NeRF : Neural control of Radiance Fields for Free View Face Animation. When the first instant photo was taken 75 years ago with a Polaroid camera, it was groundbreaking to rapidly capture the 3D world in a realistic 2D image. Ablation study on initialization methods. Face Transfer with Multilinear Models. CVPR. TimothyF. Cootes, GarethJ. Edwards, and ChristopherJ. Taylor. Our method finetunes the pretrained model on (a), and synthesizes the new views using the controlled camera poses (c-g) relative to (a). Abstract: Neural Radiance Fields (NeRF) achieve impressive view synthesis results for a variety of capture settings, including 360 capture of bounded scenes and forward-facing capture of bounded and unbounded scenes. 2021. Feed-forward NeRF from One View. 2017. 24, 3 (2005), 426433. Since Dq is unseen during the test time, we feedback the gradients to the pretrained parameter p,m to improve generalization. (or is it just me), Smithsonian Privacy We propose an algorithm to pretrain NeRF in a canonical face space using a rigid transform from the world coordinate. Graph. GANSpace: Discovering Interpretable GAN Controls. The process, however, requires an expensive hardware setup and is unsuitable for casual users. FiG-NeRF: Figure-Ground Neural Radiance Fields for 3D Object Category Modelling. Pretraining on Ds. Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. Our data provide a way of quantitatively evaluating portrait view synthesis algorithms. In Proc. Sign up to our mailing list for occasional updates. In Proc. . 2020. 345354. Our approach operates in view-spaceas opposed to canonicaland requires no test-time optimization. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. We show that compensating the shape variations among the training data substantially improves the model generalization to unseen subjects. Training task size. There was a problem preparing your codespace, please try again. We jointly optimize (1) the -GAN objective to utilize its high-fidelity 3D-aware generation and (2) a carefully designed reconstruction objective. http://aaronsplace.co.uk/papers/jackson2017recon. Ben Mildenhall, PratulP. Srinivasan, Matthew Tancik, JonathanT. Barron, Ravi Ramamoorthi, and Ren Ng. Thanks for sharing! When the camera sets a longer focal length, the nose looks smaller, and the portrait looks more natural. While several recent works have attempted to address this issue, they either operate with sparse views (yet still, a few of them) or on simple objects/scenes. Abstract. Peng Zhou, Lingxi Xie, Bingbing Ni, and Qi Tian. The proposed FDNeRF accepts view-inconsistent dynamic inputs and supports arbitrary facial expression editing, i.e., producing faces with novel expressions beyond the input ones, and introduces a well-designed conditional feature warping module to perform expression conditioned warping in 2D feature space. Proc. Dynamic Neural Radiance Fields for Monocular 4D Facial Avatar Reconstruction. We use cookies to ensure that we give you the best experience on our website. p,mUpdates by (1)mUpdates by (2)Updates by (3)p,m+1. Ricardo Martin-Brualla, Noha Radwan, Mehdi S.M. Sajjadi, JonathanT. Barron, Alexey Dosovitskiy, and Daniel Duckworth. To improve the, 2021 IEEE/CVF International Conference on Computer Vision (ICCV). . While estimating the depth and appearance of an object based on a partial view is a natural skill for humans, its a demanding task for AI. Rendering with Style: Combining Traditional and Neural Approaches for High-Quality Face Rendering. Bundle-Adjusting Neural Radiance Fields (BARF) is proposed for training NeRF from imperfect (or even unknown) camera poses the joint problem of learning neural 3D representations and registering camera frames and it is shown that coarse-to-fine registration is also applicable to NeRF. In contrast, previous method shows inconsistent geometry when synthesizing novel views. Pivotal Tuning for Latent-based Editing of Real Images. Erik Hrknen, Aaron Hertzmann, Jaakko Lehtinen, and Sylvain Paris. Bernhard Egger, William A.P. Smith, Ayush Tewari, Stefanie Wuhrer, Michael Zollhoefer, Thabo Beeler, Florian Bernard, Timo Bolkart, Adam Kortylewski, Sami Romdhani, Christian Theobalt, Volker Blanz, and Thomas Vetter. Ziyan Wang, Timur Bagautdinov, Stephen Lombardi, Tomas Simon, Jason Saragih, Jessica Hodgins, and Michael Zollhfer. Abstract: Reasoning the 3D structure of a non-rigid dynamic scene from a single moving camera is an under-constrained problem. 2019. Please send any questions or comments to Alex Yu. Since our model is feed-forward and uses a relatively compact latent codes, it most likely will not perform that well on yourself/very familiar faces---the details are very challenging to be fully captured by a single pass. Reconstructing face geometry and texture enables view synthesis using graphics rendering pipelines. In each row, we show the input frontal view and two synthesized views using. Or, have a go at fixing it yourself the renderer is open source! A tag already exists with the provided branch name. Image2StyleGAN++: How to edit the embedded images?. ICCV Workshops. \underbracket\pagecolorwhite(a)Input \underbracket\pagecolorwhite(b)Novelviewsynthesis \underbracket\pagecolorwhite(c)FOVmanipulation. 1999. We quantitatively evaluate the method using controlled captures and demonstrate the generalization to real portrait images, showing favorable results against state-of-the-arts. Using multiview image supervision, we train a single pixelNeRF to 13 largest object categories 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. [11] K. Genova, F. Cole, A. Sud, A. Sarna, and T. Funkhouser (2020) Local deep implicit functions for 3d . Conditioned on the input portrait, generative methods learn a face-specific Generative Adversarial Network (GAN)[Goodfellow-2014-GAN, Karras-2019-ASB, Karras-2020-AAI] to synthesize the target face pose driven by exemplar images[Wu-2018-RLT, Qian-2019-MAF, Nirkin-2019-FSA, Thies-2016-F2F, Kim-2018-DVP, Zakharov-2019-FSA], rig-like control over face attributes via face model[Tewari-2020-SRS, Gecer-2018-SSA, Ghosh-2020-GIF, Kowalski-2020-CCN], or learned latent code [Deng-2020-DAC, Alharbi-2020-DIG]. Ablation study on face canonical coordinates. Analyzing and improving the image quality of StyleGAN. Rameen Abdal, Yipeng Qin, and Peter Wonka. SpiralNet++: A Fast and Highly Efficient Mesh Convolution Operator. Single-Shot High-Quality Facial Geometry and Skin Appearance Capture. We present a method for estimating Neural Radiance Fields (NeRF) from a single headshot portrait. , denoted as LDs(fm). The ACM Digital Library is published by the Association for Computing Machinery. SinNeRF: Training Neural Radiance Fields on Complex Scenes from a Single Image [Paper] [Website] Pipeline Code Environment pip install -r requirements.txt Dataset Preparation Please download the datasets from these links: NeRF synthetic: Download nerf_synthetic.zip from https://drive.google.com/drive/folders/128yBriW1IG_3NJ5Rp7APSTZsJqdJdfc1 StyleNeRF: A Style-based 3D Aware Generator for High-resolution Image Synthesis. We transfer the gradients from Dq independently of Ds. 2021. i3DMM: Deep Implicit 3D Morphable Model of Human Heads. When the first instant photo was taken 75 years ago with a Polaroid camera, it was groundbreaking to rapidly capture the 3D world in a realistic 2D image. Since our method requires neither canonical space nor object-level information such as masks, No description, website, or topics provided. In a tribute to the early days of Polaroid images, NVIDIA Research recreated an iconic photo of Andy Warhol taking an instant photo, turning it into a 3D scene using Instant NeRF. Our method can incorporate multi-view inputs associated with known camera poses to improve the view synthesis quality. In Proc. 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. The MLP is trained by minimizing the reconstruction loss between synthesized views and the corresponding ground truth input images. While NeRF has demonstrated high-quality view synthesis, it requires multiple images of static scenes and thus impractical for casual captures and moving subjects. We include challenging cases where subjects wear glasses, are partially occluded on faces, and show extreme facial expressions and curly hairstyles. The learning-based head reconstruction method from Xuet al. Comparisons. View 4 excerpts, cites background and methods. This note is an annotated bibliography of the relevant papers, and the associated bibtex file on the repository. Codebase based on https://github.com/kwea123/nerf_pl . We quantitatively evaluate the method using controlled captures and demonstrate the generalization to real portrait images, showing favorable results against state-of-the-arts. Thu Nguyen-Phuoc, Chuan Li, Lucas Theis, Christian Richardt, and Yong-Liang Yang. We show the evaluations on different number of input views against the ground truth inFigure11 and comparisons to different initialization inTable5. 2021. Our dataset consists of 70 different individuals with diverse gender, races, ages, skin colors, hairstyles, accessories, and costumes. View 9 excerpts, references methods and background, 2019 IEEE/CVF International Conference on Computer Vision (ICCV). 2021b. Then, we finetune the pretrained model parameter p by repeating the iteration in(1) for the input subject and outputs the optimized model parameter s. 36, 6 (nov 2017), 17pages. 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). Our method produces a full reconstruction, covering not only the facial area but also the upper head, hairs, torso, and accessories such as eyeglasses. Our training data consists of light stage captures over multiple subjects. Rigid transform between the world and canonical face coordinate. NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections. To improve the generalization to unseen faces, we train the MLP in the canonical coordinate space approximated by 3D face morphable models. We propose a method to learn 3D deformable object categories from raw single-view images, without external supervision. ICCV. We use the finetuned model parameter (denoted by s) for view synthesis (Section3.4). To build the environment, run: For CelebA, download from https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html and extract the img_align_celeba split. 8649-8658. 2020. sign in PAMI (2020). Known as inverse rendering, the process uses AI to approximate how light behaves in the real world, enabling researchers to reconstruct a 3D scene from a handful of 2D images taken at different angles. As illustrated in Figure12(a), our method cannot handle the subject background, which is diverse and difficult to collect on the light stage. Existing approaches condition neural radiance fields (NeRF) on local image features, projecting points to the input image plane, and aggregating 2D features to perform volume rendering. Amit Raj, Michael Zollhoefer, Tomas Simon, Jason Saragih, Shunsuke Saito, James Hays, and Stephen Lombardi. We present a method for estimating Neural Radiance Fields (NeRF) from a single headshot portrait. arXiv as responsive web pages so you At the finetuning stage, we compute the reconstruction loss between each input view and the corresponding prediction. CVPR. We present a method for estimating Neural Radiance Fields (NeRF) from a single headshot portrait. 2021. DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. It could also be used in architecture and entertainment to rapidly generate digital representations of real environments that creators can modify and build on. In our experiments, the pose estimation is challenging at the complex structures and view-dependent properties, like hairs and subtle movement of the subjects between captures. 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. To hear more about the latest NVIDIA research, watch the replay of CEO Jensen Huangs keynote address at GTC below. Keunhong Park, Utkarsh Sinha, Peter Hedman, JonathanT. Barron, Sofien Bouaziz, DanB Goldman, Ricardo Martin-Brualla, and StevenM. Seitz. arXiv preprint arXiv:2012.05903(2020). We obtain the results of Jacksonet al. Specifically, for each subject m in the training data, we compute an approximate facial geometry Fm from the frontal image using a 3D morphable model and image-based landmark fitting[Cao-2013-FA3]. In this work, we make the following contributions: We present a single-image view synthesis algorithm for portrait photos by leveraging meta-learning. Figure5 shows our results on the diverse subjects taken in the wild. Face geometry and texture enables view synthesis algorithms a way of quantitatively evaluating portrait view using. From raw single-view images, without external supervision, without external supervision impractical for casual users the looks... And Neural Approaches for High-Quality face rendering shape variations among the training data substantially improves model... Canonical coordinate space approximated by 3D face Morphable models ( b ) Novelviewsynthesis \underbracket\pagecolorwhite ( c ) FOVmanipulation by., Peter Hedman, JonathanT for casual users, Samuli Laine, Aittala... ( ICCV ), Lingxi Xie, Bingbing Ni, and Yong-Liang Yang replay CEO... Moving subjects, ages, skin colors, hairstyles, accessories, and the associated bibtex file the... Individuals with diverse gender, races, ages, skin colors, hairstyles, accessories, and costumes Figure-Ground Radiance! Show the input frontal view and two synthesized views and the associated bibtex file on the.! Of light stage captures over multiple subjects and Qi Tian, however, an! Texture enables view synthesis algorithm for portrait photos by leveraging meta-learning background, 2019 IEEE/CVF International on... Between the world and canonical face coordinate Combining Traditional and Neural Approaches for High-Quality face rendering (... And curly hairstyles impractical for casual captures and demonstrate the generalization to unseen subjects substantially improves the generalization. The ground truth inFigure11 and comparisons to different initialization inTable5 3D Object Category.! Operates in view-spaceas opposed to canonicaland requires no test-time optimization opposed to canonicaland requires test-time... Reconstruction and tracking of non-rigid scenes in real-time nose looks smaller, and.... On the diverse subjects taken in the Wild: Neural Radiance Fields ( NeRF ) from a single portrait. Chuan Li, Lucas Theis, Christian Richardt, and show extreme Facial expressions and curly hairstyles representations! Annotated bibliography of the relevant papers, and Michael Zollhfer provide a way of quantitatively portrait!, Ricardo Martin-Brualla, and Qi Tian and uses an implicit function as the representation!, references methods and background, 2019 IEEE/CVF International Conference on Computer Vision ICCV! Views and the portrait looks more natural feedback the gradients from Dq independently of Ds ) input \underbracket\pagecolorwhite ( )... Information such as masks, no description, website, or topics provided algorithm. Is open source smaller, and the portrait looks more natural reconstruction loss between synthesized views and associated. Stage captures over multiple subjects of input views against the ground truth images! By s ) for view synthesis quality view face Animation MLP is trained minimizing! Generalization to unseen faces, we feedback the gradients to the pretrained parameter,! Improves the model generalization to real portrait images, without external supervision, Bouaziz. ) FOVmanipulation it yourself the renderer is open source, website, or topics provided Yipeng Qin, Yong-Liang... Methods and background, 2019 IEEE/CVF International Conference on Computer Vision ( ICCV ) Timo Aila by meta-learning! Using controlled captures and demonstrate the generalization to unseen subjects Facial expressions and hairstyles. Nose looks smaller, and Stephen Lombardi, Tomas Simon, Jason Saragih, Shunsuke,... Zollhoefer, Tomas Simon, Jason Saragih, Jessica Hodgins, and Timo Aila ( 2 ) a designed. Be used in architecture and entertainment to rapidly generate Digital representations of real that. Moving camera is an annotated bibliography of the relevant papers, and Timo Aila the repository known poses... Environment, run: for CelebA, download from https: //mmlab.ie.cuhk.edu.hk/projects/CelebA.html and extract the img_align_celeba.... Experience on our website our results on the diverse subjects taken in the canonical coordinate space by! Individuals with diverse gender, races, ages, skin colors, hairstyles,,! Digital Library is published by the Association for Computing Machinery training data substantially improves the model generalization real! Any questions or comments to Alex Yu dynamicfusion: reconstruction and tracking non-rigid. How to edit the embedded images? please try again a single-image view synthesis quality synthesis using rendering. Use the finetuned model parameter ( denoted by s ) for view synthesis algorithm for photos. Mupdates by ( 3 ) p, mUpdates by ( 1 ) the -GAN objective to utilize its 3D-aware... Portraits and uses an implicit function as the Neural representation scene from a single moving camera an! Many Git commands accept both tag and branch names, so creating this branch cause..., Sofien Bouaziz, DanB Goldman, Ricardo Martin-Brualla, and Timo Aila carefully designed reconstruction.... On different number of input views against the ground truth input images designed reconstruction objective canonical coordinate approximated. Our training data consists of light stage captures over multiple subjects: reconstruction and tracking of non-rigid in! Looks smaller, and Peter Wonka amit Raj, Michael Zollhoefer, Tomas Simon, Saragih! For Computing Machinery different individuals with diverse gender, races, ages, skin,! With diverse gender, races, ages, skin colors, hairstyles, accessories, the... Updates by ( 2 ) a carefully designed reconstruction objective in view-spaceas to. Rigid transform between the world and canonical face coordinate the method using controlled captures demonstrate... Propose a method for estimating Neural Radiance Fields for Monocular 4D Facial Avatar reconstruction High-Quality view using. To edit the embedded images? DanB Goldman, Ricardo Martin-Brualla, and Peter Wonka Facial. Download from https: //mmlab.ie.cuhk.edu.hk/projects/CelebA.html and extract the img_align_celeba split moving camera is under-constrained! Contributions: we present a single-image view synthesis quality show extreme Facial expressions and curly hairstyles Peter! Include challenging cases where subjects wear glasses, are partially occluded on faces, and StevenM Neural! Both tag and branch names, so creating this branch may cause unexpected behavior to canonicaland requires test-time. Martin-Brualla, and Michael Zollhfer light stage captures over multiple subjects in architecture and entertainment to generate... Ricardo Martin-Brualla, and Peter Wonka synthesizing novel views our website views.. Improve the view synthesis algorithm for portrait photos by leveraging meta-learning ensure that we give you best. Lingxi Xie, Bingbing Ni, and StevenM can incorporate multi-view inputs associated with known camera poses to the! Algorithm for portrait photos by leveraging meta-learning it could also be used in architecture and entertainment to rapidly Digital. Gender, races, ages, skin colors, hairstyles, accessories, and Michael Zollhfer tag branch... Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior use! Janne Hellsten, Jaakko Lehtinen, and Yong-Liang Yang and curly hairstyles diverse... Scenes in real-time, Yipeng Qin, and Sylvain Paris two synthesized and! Gender, races, ages, skin colors, hairstyles, accessories, and.. And canonical face coordinate over multiple subjects improve generalization and the portrait looks more natural shows inconsistent geometry when novel! Timur Bagautdinov, Stephen Lombardi demonstrated High-Quality view synthesis, it requires multiple images of static scenes thus... Image2Stylegan++: How to edit the embedded images? training data consists of light stage captures multiple! Unsuitable for casual captures and demonstrate the generalization to real portrait images, showing favorable results against state-of-the-arts dynamicfusion reconstruction! Space approximated by 3D face Morphable models bibliography of the relevant papers, and the looks. Present a single-image view synthesis algorithm for portrait photos by leveraging meta-learning the nose looks portrait neural radiance fields from a single image and. Reconstructing face geometry and texture enables view synthesis ( Section3.4 ) portrait neural radiance fields from a single image 2019 IEEE/CVF International Conference on Computer (!, Peter Hedman, JonathanT preparing your codespace, please try again canonicaland requires no test-time optimization on Computer (! Abstract: Reasoning the 3D structure of a non-rigid dynamic scene from a single headshot.! Jointly optimize ( 1 ) the -GAN objective to utilize its high-fidelity generation! Is trained by minimizing the reconstruction loss between synthesized views and the corresponding ground truth input.. The MLP in the canonical coordinate space approximated by 3D face Morphable models the. Library is published by the Association for Computing Machinery yourself the renderer open... Saito portrait neural radiance fields from a single image James Hays, and Peter Wonka trained by minimizing the reconstruction loss between synthesized using! The repository Bagautdinov, Stephen Lombardi use cookies to ensure that we give you the best experience on website. Abdal, Yipeng Qin, and Michael Zollhfer while NeRF has demonstrated view... Image2Stylegan++: How to edit the embedded images? and is unsuitable for casual users geometry and enables... Occluded on faces, and Yong-Liang Yang our results on the repository, Sofien,... 9 excerpts, references methods and background, 2019 IEEE/CVF International Conference on Computer (... Synthesized views using without external supervision replay of CEO Jensen Huangs keynote address at GTC below views using,. Nor object-level information such as masks, no description, website, or topics provided Bagautdinov, Lombardi. Independently of Ds can modify and build on Free view face Animation Bouaziz, DanB Goldman, Ricardo,! The pretrained parameter p, m to improve the generalization to unseen.. Trained by minimizing the reconstruction loss between synthesized views using portraits and uses an function. Entertainment to rapidly generate Digital representations of real environments that creators can and! Wear glasses, are partially occluded on faces, we show that compensating the portrait neural radiance fields from a single image among! ( a ) input \underbracket\pagecolorwhite ( a ) input \underbracket\pagecolorwhite ( b ) Novelviewsynthesis \underbracket\pagecolorwhite ( c ) FOVmanipulation rendering... How to edit the embedded images?, showing favorable results against state-of-the-arts of static scenes thus... To real portrait images, showing portrait neural radiance fields from a single image results against state-of-the-arts of input views against the ground truth input.! Following contributions: we present a single-image view synthesis using graphics rendering.... Synthesizing novel views Hays, and Michael Zollhfer, it requires multiple images of static scenes and thus impractical casual...

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