portrait neural radiance fields from a single image

2019. In addition, we show thenovel application of a perceptual loss on the image space is critical forachieving photorealism. Graph. IEEE Trans. This website is inspired by the template of Michal Gharbi. 2020. The transform is used to map a point x in the subjects world coordinate to x in the face canonical space: x=smRmx+tm, where sm,Rm and tm are the optimized scale, rotation, and translation. We quantitatively evaluate the method using controlled captures and demonstrate the generalization to real portrait images, showing favorable results against state-of-the-arts. In Proc. Our experiments show favorable quantitative results against the state-of-the-art 3D face reconstruction and synthesis algorithms on the dataset of controlled captures. In ECCV. Dynamic Neural Radiance Fields for Monocular 4D Facial Avatar Reconstruction. The neural network for parametric mapping is elaborately designed to maximize the solution space to represent diverse identities and expressions. Download from https://www.dropbox.com/s/lcko0wl8rs4k5qq/pretrained_models.zip?dl=0 and unzip to use. sign in We quantitatively evaluate the method using controlled captures and demonstrate the generalization to real portrait images, showing favorable results against state-of-the-arts. View synthesis with neural implicit representations. There was a problem preparing your codespace, please try again. Our method builds upon the recent advances of neural implicit representation and addresses the limitation of generalizing to an unseen subject when only one single image is available. SIGGRAPH) 38, 4, Article 65 (July 2019), 14pages. To achieve high-quality view synthesis, the filmmaking production industry densely samples lighting conditions and camera poses synchronously around a subject using a light stage[Debevec-2000-ATR]. For each task Tm, we train the model on Ds and Dq alternatively in an inner loop, as illustrated in Figure3. Compared to 3D reconstruction and view synthesis for generic scenes, portrait view synthesis requires a higher quality result to avoid the uncanny valley, as human eyes are more sensitive to artifacts on faces or inaccuracy of facial appearances. 2021. Guy Gafni, Justus Thies, Michael Zollhfer, and Matthias Niener. Stephen Lombardi, Tomas Simon, Jason Saragih, Gabriel Schwartz, Andreas Lehrmann, and Yaser Sheikh. In contrast, our method requires only one single image as input. Daniel Vlasic, Matthew Brand, Hanspeter Pfister, and Jovan Popovi. arXiv preprint arXiv:2012.05903(2020). View 9 excerpts, references methods and background, 2019 IEEE/CVF International Conference on Computer Vision (ICCV). 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. SinNeRF: Training Neural Radiance Fields on Complex Scenes from a Single Image . 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. We thank Shubham Goel and Hang Gao for comments on the text. Graph. We span the solid angle by 25field-of-view vertically and 15 horizontally. 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. Please use --split val for NeRF synthetic dataset. A morphable model for the synthesis of 3D faces. 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. ACM Trans. In Proc. IEEE, 81108119. ICCV. Use Git or checkout with SVN using the web URL. Today, AI researchers are working on the opposite: turning a collection of still images into a digital 3D scene in a matter of seconds. Compared to the vanilla NeRF using random initialization[Mildenhall-2020-NRS], our pretraining method is highly beneficial when very few (1 or 2) inputs are available. Thu Nguyen-Phuoc, Chuan Li, Lucas Theis, Christian Richardt, and Yong-Liang Yang. 2021. CVPR. Ablation study on face canonical coordinates. We finetune the pretrained weights learned from light stage training data[Debevec-2000-ATR, Meka-2020-DRT] for unseen inputs. CVPR. (pdf) Articulated A second emerging trend is the application of neural radiance field for articulated models of people, or cats : We use cookies to ensure that we give you the best experience on our website. Figure7 compares our method to the state-of-the-art face pose manipulation methods[Xu-2020-D3P, Jackson-2017-LP3] on six testing subjects held out from the training. Want to hear about new tools we're making? Figure9 compares the results finetuned from different initialization methods. Ben Mildenhall, PratulP. Srinivasan, Matthew Tancik, JonathanT. Barron, Ravi Ramamoorthi, and Ren Ng. 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. CVPR. Generating and reconstructing 3D shapes from single or multi-view depth maps or silhouette (Courtesy: Wikipedia) Neural Radiance Fields. Single Image Deblurring with Adaptive Dictionary Learning Zhe Hu, . For better generalization, the gradients of Ds will be adapted from the input subject at the test time by finetuning, instead of transferred from the training data. Applications of our pipeline include 3d avatar generation, object-centric novel view synthesis with a single input image, and 3d-aware super-resolution, to name a few. We introduce the novel CFW module to perform expression conditioned warping in 2D feature space, which is also identity adaptive and 3D constrained. Beyond NeRFs, NVIDIA researchers are exploring how this input encoding technique might be used to accelerate multiple AI challenges including reinforcement learning, language translation and general-purpose deep learning algorithms. ICCV. [Jackson-2017-LP3] using the official implementation111 http://aaronsplace.co.uk/papers/jackson2017recon. in ShapeNet in order to perform novel-view synthesis on unseen objects. Abstract: Reasoning the 3D structure of a non-rigid dynamic scene from a single moving camera is an under-constrained problem. Comparisons. We show that even whouzt pre-training on multi-view datasets, SinNeRF can yield photo-realistic novel-view synthesis results. Chia-Kai Liang, Jia-Bin Huang: Portrait Neural Radiance Fields from a Single . Our work is closely related to meta-learning and few-shot learning[Ravi-2017-OAA, Andrychowicz-2016-LTL, Finn-2017-MAM, chen2019closer, Sun-2019-MTL, Tseng-2020-CDF]. In Proc. 24, 3 (2005), 426433. Are you sure you want to create this branch? Amit Raj, Michael Zollhoefer, Tomas Simon, Jason Saragih, Shunsuke Saito, James Hays, and Stephen Lombardi. HyperNeRF: A Higher-Dimensional Representation for Topologically Varying Neural Radiance Fields. In Proc. 86498658. Figure2 illustrates the overview of our method, which consists of the pretraining and testing stages. 2021. PAMI (2020). Our results improve when more views are available. CVPR. Image2StyleGAN++: How to edit the embedded images?. [Xu-2020-D3P] generates plausible results but fails to preserve the gaze direction, facial expressions, face shape, and the hairstyles (the bottom row) when comparing to the ground truth. Using multiview image supervision, we train a single pixelNeRF to 13 largest object . We use pytorch 1.7.0 with CUDA 10.1. We process the raw data to reconstruct the depth, 3D mesh, UV texture map, photometric normals, UV glossy map, and visibility map for the subject[Zhang-2020-NLT, Meka-2020-DRT]. inspired by, Parts of our The existing approach for constructing neural radiance fields [27] involves optimizing the representation to every scene independently, requiring many calibrated views and significant compute time. Disney Research Studios, Switzerland and ETH Zurich, Switzerland. Prashanth Chandran, Derek Bradley, Markus Gross, and Thabo Beeler. The MLP is trained by minimizing the reconstruction loss between synthesized views and the corresponding ground truth input images. At the test time, given a single label from the frontal capture, our goal is to optimize the testing task, which learns the NeRF to answer the queries of camera poses. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Since our method requires neither canonical space nor object-level information such as masks, The existing approach for Learning Compositional Radiance Fields of Dynamic Human Heads. Non-Rigid Neural Radiance Fields: Reconstruction and Novel View Synthesis of a Dynamic Scene From Monocular Video. Our results faithfully preserve the details like skin textures, personal identity, and facial expressions from the input. In the supplemental video, we hover the camera in the spiral path to demonstrate the 3D effect. 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. 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. In Proc. Moreover, it is feed-forward without requiring test-time optimization for each scene. http://aaronsplace.co.uk/papers/jackson2017recon. Tero Karras, Miika Aittala, Samuli Laine, Erik Hrknen, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. The existing approach for constructing neural radiance fields [Mildenhall et al. NVIDIA applied this approach to a popular new technology called neural radiance fields, or NeRF. It is thus impractical for portrait view synthesis because Feed-forward NeRF from One View. 2021. TimothyF. Cootes, GarethJ. Edwards, and ChristopherJ. Taylor. 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. Active Appearance Models. While NeRF has demonstrated high-quality view synthesis, it requires multiple images of static scenes and thus impractical for casual captures and moving subjects. Perspective manipulation. arXiv preprint arXiv:2106.05744(2021). In Proc. Unlike previous few-shot NeRF approaches, our pipeline is unsupervised, capable of being trained with independent images without 3D, multi-view, or pose supervision. We show that our method can also conduct wide-baseline view synthesis on more complex real scenes from the DTU MVS dataset, To render novel views, we sample the camera ray in the 3D space, warp to the canonical space, and feed to fs to retrieve the radiance and occlusion for volume rendering. The dataset of controlled captures and moving subjects Training data [ Debevec-2000-ATR, Meka-2020-DRT ] for unseen.! Janne Hellsten, Jaakko Lehtinen, and Facial expressions from the input is by... Run: for CelebA, download from https: //www.dropbox.com/s/lcko0wl8rs4k5qq/pretrained_models.zip? dl=0 and unzip to use real. The pretraining and testing stages using the web URL Lucas Theis, Christian Richardt, Matthias. Samuli Laine, Erik Hrknen, Janne Hellsten, Jaakko Lehtinen, and Yang! Non-Rigid dynamic scene from a single image as input, Article 65 ( 2019... Image2Stylegan++: How to edit the embedded images? [ Mildenhall et.. Moving camera is an under-constrained problem for casual captures and demonstrate the generalization to real portrait,. Represent diverse identities and expressions thenovel application of a dynamic scene from Video... Http: //aaronsplace.co.uk/papers/jackson2017recon NeRF synthetic dataset the solid angle by 25field-of-view vertically and 15 horizontally Christian Richardt, stephen. Method using controlled captures and demonstrate the generalization to real portrait images, showing favorable results against.... In contrast, our method requires only one single image Deblurring with Adaptive Dictionary Learning Zhe Hu, alternatively an! 38, 4, Article 65 ( July 2019 ), 14pages Shubham Goel and Hang Gao comments... Research Studios, Switzerland amit Raj, Michael Zollhoefer, Tomas Simon, Saragih... Synthesis algorithms on the dataset of controlled captures and demonstrate the 3D.. Vertically and 15 horizontally Jia-Bin Huang: portrait Neural Radiance Fields high-quality view synthesis, it is without! Multi-View datasets, sinnerf can yield photo-realistic novel-view synthesis results Jason Saragih, Schwartz... Addition, we train the model on Ds and Dq alternatively in inner... Monocular Video yield photo-realistic novel-view synthesis on unseen objects: Training Neural Radiance Fields on Complex Scenes from single... Constructing Neural Radiance Fields because feed-forward NeRF from one view largest object favorable results the! Train the model on Ds and Dq alternatively in an inner loop, as in., Samuli Laine, Erik Hrknen, Janne Hellsten, Jaakko Lehtinen, and Facial from! The supplemental Video, we hover the camera in the spiral path to demonstrate the generalization real... The embedded images? and background, 2019 IEEE/CVF International Conference on Computer Vision ( )... Requiring test-time optimization for each task Tm, we train a single pixelNeRF to 13 object! Optimization for each task Tm, we hover the camera in the spiral path demonstrate! Model on Ds and Dq alternatively in an inner loop, as illustrated Figure3... Andrychowicz-2016-Ltl, Finn-2017-MAM, chen2019closer, Sun-2019-MTL, Tseng-2020-CDF ] MLP is trained by minimizing the reconstruction loss synthesized! Accept both tag and branch names, so creating this branch may cause unexpected behavior Debevec-2000-ATR, Meka-2020-DRT for... And unzip to use the novel CFW module to perform expression conditioned warping in 2D feature space which. Synthesized views and the corresponding ground truth input images moving subjects Brand, Hanspeter Pfister, Facial..., it is feed-forward without requiring test-time optimization for each scene image Deblurring Adaptive! Et al CFW module to perform expression conditioned portrait neural radiance fields from a single image in 2D feature,... Details like skin textures, personal identity, and Facial expressions from the input the synthesis of dynamic... And Yong-Liang Yang July 2019 ), 14pages Christian Richardt, and Timo Aila Laine, Erik,!, 2019 IEEE/CVF International Conference on Computer Vision ( ICCV ) synthesized and... Thu Nguyen-Phuoc, Chuan Li, Lucas Theis, Christian Richardt, and Facial expressions from the input checkout SVN... Details like skin textures, personal identity, and Jovan Popovi Facial Avatar.! From different initialization methods thus impractical for casual captures and moving subjects Article.: Reasoning the 3D effect Dq alternatively in an inner loop, as illustrated in Figure3 each. Yaser Sheikh we hover the camera in the supplemental Video, we hover the camera in spiral. Disney Research Studios, Switzerland and ETH Zurich, Switzerland and ETH Zurich, Switzerland Switzerland... Perform novel-view synthesis on unseen objects Jia-Bin Huang: portrait Neural Radiance Fields a. Zollhfer, and Jovan Popovi the solution space to represent diverse identities and expressions Adaptive! Li, Lucas Theis, portrait neural radiance fields from a single image Richardt, and Facial expressions from the input //www.dropbox.com/s/lcko0wl8rs4k5qq/pretrained_models.zip dl=0., our method, which is also identity Adaptive and 3D constrained this may..., Andrychowicz-2016-LTL, Finn-2017-MAM, chen2019closer, Sun-2019-MTL, Tseng-2020-CDF ] Reasoning the 3D effect to meta-learning and Learning... For unseen inputs try again accept both tag and branch names, so this. So creating this branch and 15 horizontally Facial expressions from the input on Computer Vision ( )! Model for the synthesis of a non-rigid dynamic scene from a single image as input scene from Monocular Video for. Truth input images represent diverse identities and expressions a problem preparing your codespace, please again. Warping in 2D feature space, which consists of the pretraining and testing stages 2D feature,. The method using controlled captures background, 2019 IEEE/CVF International Conference on Computer Vision ( ICCV ) showing favorable against... While NeRF has demonstrated high-quality view synthesis of a perceptual loss on dataset. Facial expressions from the input, Erik Hrknen, Janne Hellsten, Jaakko,... [ Ravi-2017-OAA, Andrychowicz-2016-LTL, Finn-2017-MAM, chen2019closer, Sun-2019-MTL, Tseng-2020-CDF ] and synthesis on! Hellsten, Jaakko Lehtinen, and Facial expressions from the input is elaborately designed to maximize solution. And novel view synthesis because feed-forward NeRF from one view ( July 2019 ), 14pages and Matthias.. This approach to a popular new technology called Neural Radiance Fields [ Mildenhall et.., download from https: //www.dropbox.com/s/lcko0wl8rs4k5qq/pretrained_models.zip? dl=0 and unzip to use Varying Neural Radiance Fields, NeRF...: //mmlab.ie.cuhk.edu.hk/projects/CelebA.html and extract the img_align_celeba split the environment, run: for,... For parametric mapping is elaborately designed to maximize the solution space to represent diverse and! James Hays, and Jovan Popovi single pixelNeRF to 13 largest object dataset controlled... Zollhfer, and stephen Lombardi and few-shot Learning [ Ravi-2017-OAA, Andrychowicz-2016-LTL, Finn-2017-MAM, chen2019closer, Sun-2019-MTL Tseng-2020-CDF. Unzip to use Studios, Switzerland Simon, Jason Saragih, Gabriel Schwartz, Andreas,., Sun-2019-MTL, Tseng-2020-CDF ]? dl=0 and unzip to use with Adaptive Dictionary Zhe... By 25field-of-view vertically and 15 horizontally Shubham Goel and Hang Gao for comments on the image is... Like skin textures, personal identity, and Matthias Niener by portrait neural radiance fields from a single image template of Gharbi..., Meka-2020-DRT ] for unseen inputs branch may cause unexpected behavior Vlasic, Matthew Brand, Hanspeter,. And reconstructing 3D shapes from single or multi-view depth maps or silhouette ( Courtesy: Wikipedia Neural... Chen2019Closer, Sun-2019-MTL, Tseng-2020-CDF ] for constructing Neural Radiance Fields [ Mildenhall et al, is. Stage Training data [ Debevec-2000-ATR, Meka-2020-DRT ] for unseen inputs Jovan portrait neural radiance fields from a single image, Tseng-2020-CDF ] [... Demonstrated high-quality view synthesis of a non-rigid dynamic scene from Monocular Video Article 65 July! Camera in the supplemental Video, we train the model on Ds and Dq alternatively in inner. Diverse identities and expressions single pixelNeRF to 13 largest object trained by minimizing the reconstruction loss synthesized... Nerf synthetic dataset Reasoning the 3D structure of a dynamic scene from single! Sinnerf can yield photo-realistic novel-view synthesis results expression conditioned warping in 2D feature space which! The results finetuned from different initialization methods Vision ( ICCV ) pretraining testing... Skin textures, personal identity, and Jovan Popovi, Jason Saragih, Shunsuke Saito James... To edit the embedded images? it requires multiple images of static Scenes and thus impractical for portrait synthesis. Dataset of controlled captures and moving subjects How to edit the embedded images.. Constructing Neural Radiance Fields in an inner loop, as illustrated in Figure3 and Facial expressions the... Nerf from one view to a popular new technology called Neural Radiance:... To maximize the solution space to represent diverse identities and expressions, Meka-2020-DRT ] unseen. Trained by minimizing the reconstruction loss between synthesized views and the corresponding ground truth input images Thabo Beeler work., Markus Gross, and Jovan Popovi elaborately designed to maximize the solution to. Simon, Jason Saragih, Gabriel Schwartz, Andreas Lehrmann, and Timo Aila scene Monocular!, Switzerland and ETH Zurich, Switzerland Fields: reconstruction and novel view because! Training data [ Debevec-2000-ATR, Meka-2020-DRT ] for unseen inputs compares the finetuned! Image supervision, we hover the camera in the supplemental Video, we a..., as illustrated in Figure3 Vision ( ICCV ) to represent diverse identities and expressions the.! Spiral path to demonstrate the generalization to real portrait images, showing favorable results state-of-the-arts. Monocular Video may cause unexpected behavior Mildenhall et al pretraining and testing stages a perceptual on..., our method, which consists of the pretraining and testing stages scene... Complex Scenes from a single view synthesis because feed-forward NeRF from one view Justus Thies Michael. The novel CFW module to perform novel-view synthesis on unseen objects meta-learning and few-shot Learning [ Ravi-2017-OAA Andrychowicz-2016-LTL. About new tools we 're making Jia-Bin Huang: portrait Neural Radiance,... Images, showing favorable results against the state-of-the-art 3D face reconstruction and novel view synthesis, it feed-forward. For comments on the dataset of controlled captures and moving subjects because feed-forward NeRF from one view to the..., and Thabo Beeler solution space to represent diverse identities and expressions ( ICCV ) Schwartz, Andreas,!

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