portrait neural radiance fields from a single imageportrait neural radiance fields from a single image

H3D-Net: Few-Shot High-Fidelity 3D Head Reconstruction. CVPR. While NeRF has demonstrated high-quality view synthesis, it requires multiple images of static scenes and thus impractical for casual captures and moving subjects. 1. Work fast with our official CLI. Dynamic Neural Radiance Fields for Monocular 4D Facial Avatar Reconstruction. Tianye Li, Timo Bolkart, MichaelJ. Use, Smithsonian SIGGRAPH '22: ACM SIGGRAPH 2022 Conference Proceedings. While NeRF has demonstrated high-quality view synthesis, it requires multiple images of static scenes and thus impractical for casual captures and moving subjects. Volker Blanz and Thomas Vetter. 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. arXiv preprint arXiv:2110.09788(2021). 2020. FLAME-in-NeRF : Neural control of Radiance Fields for Free View Face Animation. Codebase based on https://github.com/kwea123/nerf_pl . Learn more. In contrast, previous method shows inconsistent geometry when synthesizing novel views. Reconstructing the facial geometry from a single capture requires face mesh templates[Bouaziz-2013-OMF] or a 3D morphable model[Blanz-1999-AMM, Cao-2013-FA3, Booth-2016-A3M, Li-2017-LAM]. Initialization. Chia-Kai Liang, Jia-Bin Huang: Portrait Neural Radiance Fields from a Single . In total, our dataset consists of 230 captures. Here, we demonstrate how MoRF is a strong new step forwards towards generative NeRFs for 3D neural head modeling. The University of Texas at Austin, Austin, USA. For ShapeNet-SRN, download from https://github.com/sxyu/pixel-nerf and remove the additional layer, so that there are 3 folders chairs_train, chairs_val and chairs_test within srn_chairs. In Proc. add losses implementation, prepare for train script push, Pix2NeRF: Unsupervised Conditional -GAN for Single Image to Neural Radiance Fields Translation (CVPR 2022), https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html, https://www.dropbox.com/s/lcko0wl8rs4k5qq/pretrained_models.zip?dl=0. By virtually moving the camera closer or further from the subject and adjusting the focal length correspondingly to preserve the face area, we demonstrate perspective effect manipulation using portrait NeRF inFigure8 and the supplemental video. Anurag Ranjan, Timo Bolkart, Soubhik Sanyal, and MichaelJ. Please use --split val for NeRF synthetic dataset. DietNeRF improves the perceptual quality of few-shot view synthesis when learned from scratch, can render novel views with as few as one observed image when pre-trained on a multi-view dataset, and produces plausible completions of completely unobserved regions. We span the solid angle by 25field-of-view vertically and 15 horizontally. Our method precisely controls the camera pose, and faithfully reconstructs the details from the subject, as shown in the insets. We then feed the warped coordinate to the MLP network f to retrieve color and occlusion (Figure4). Are you sure you want to create this branch? Our method using (c) canonical face coordinate shows better quality than using (b) world coordinate on chin and eyes. 2019. 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. This is because each update in view synthesis requires gradients gathered from millions of samples across the scene coordinates and viewing directions, which do not fit into a single batch in modern GPU. 2021. Future work. (b) When the input is not a frontal view, the result shows artifacts on the hairs. CVPR. While NeRF has demonstrated high-quality view synthesis, it requires multiple images of static scenes and thus impractical for casual captures and moving subjects. View 10 excerpts, references methods and background, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE. 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. To improve the generalization to unseen faces, we train the MLP in the canonical coordinate space approximated by 3D face morphable models. Black, Hao Li, and Javier Romero. We show that even without pre-training on multi-view datasets, SinNeRF can yield photo-realistic novel-view synthesis results. Face Transfer with Multilinear Models. 2020. For everything else, email us at [emailprotected]. 2021. VictoriaFernandez Abrevaya, Adnane Boukhayma, Stefanie Wuhrer, and Edmond Boyer. 2021. Keunhong Park, Utkarsh Sinha, JonathanT. Barron, Sofien Bouaziz, DanB Goldman, StevenM. Seitz, and Ricardo Martin-Brualla. 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. After Nq iterations, we update the pretrained parameter by the following: Note that(3) does not affect the update of the current subject m, i.e.,(2), but the gradients are carried over to the subjects in the subsequent iterations through the pretrained model parameter update in(4). Non-Rigid Neural Radiance Fields: Reconstruction and Novel View Synthesis of a Dynamic Scene From Monocular Video. In Proc. 2021. a slight subject movement or inaccurate camera pose estimation degrades the reconstruction quality. In Proc. We provide pretrained model checkpoint files for the three datasets. InTable4, we show that the validation performance saturates after visiting 59 training tasks. Given a camera pose, one can synthesize the corresponding view by aggregating the radiance over the light ray cast from the camera pose using standard volume rendering. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2017. Learning a Model of Facial Shape and Expression from 4D Scans. In Proc. This note is an annotated bibliography of the relevant papers, and the associated bibtex file on the repository. Since Dq is unseen during the test time, we feedback the gradients to the pretrained parameter p,m to improve generalization. CoRR abs/2012.05903 (2020), Copyright 2023 Sanghani Center for Artificial Intelligence and Data Analytics, Sanghani Center for Artificial Intelligence and Data Analytics. NVIDIA websites use cookies to deliver and improve the website experience. In Proc. If nothing happens, download Xcode and try again. We show the evaluations on different number of input views against the ground truth inFigure11 and comparisons to different initialization inTable5. More finetuning with smaller strides benefits reconstruction quality. [1/4] 01 Mar 2023 06:04:56 Our work is a first step toward the goal that makes NeRF practical with casual captures on hand-held devices. In Proc. Sign up to our mailing list for occasional updates. PAMI PP (Oct. 2020). Project page: https://vita-group.github.io/SinNeRF/ We thank the authors for releasing the code and providing support throughout the development of this project. The existing approach for constructing neural radiance fields [Mildenhall et al. Extrapolating the camera pose to the unseen poses from the training data is challenging and leads to artifacts. 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. Left and right in (a) and (b): input and output of our method. In Proc. to use Codespaces. NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections. Generating and reconstructing 3D shapes from single or multi-view depth maps or silhouette (Courtesy: Wikipedia) Neural Radiance Fields. The disentangled parameters of shape, appearance and expression can be interpolated to achieve a continuous and morphable facial synthesis. python linear_interpolation --path=/PATH_TO/checkpoint_train.pth --output_dir=/PATH_TO_WRITE_TO/. inspired by, Parts of our 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. SpiralNet++: A Fast and Highly Efficient Mesh Convolution Operator. we capture 2-10 different expressions, poses, and accessories on a light stage under fixed lighting conditions. 2021. 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. Pix2NeRF: Unsupervised Conditional -GAN for Single Image to Neural Radiance Fields Translation We show that even whouzt pre-training on multi-view datasets, SinNeRF can yield photo-realistic novel-view synthesis results. For each task Tm, we train the model on Ds and Dq alternatively in an inner loop, as illustrated in Figure3. Without warping to the canonical face coordinate, the results using the world coordinate inFigure10(b) show artifacts on the eyes and chins. Face pose manipulation. We show that, unlike existing methods, one does not need multi-view . 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. In our experiments, applying the meta-learning algorithm designed for image classification[Tseng-2020-CDF] performs poorly for view synthesis. 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]. A learning-based method for synthesizing novel views of complex scenes using only unstructured collections of in-the-wild photographs, and applies it to internet photo collections of famous landmarks, to demonstrate temporally consistent novel view renderings that are significantly closer to photorealism than the prior state of the art. [width=1]fig/method/pretrain_v5.pdf We report the quantitative evaluation using PSNR, SSIM, and LPIPS[zhang2018unreasonable] against the ground truth inTable1. Explore our regional blogs and other social networks. sign in The result, dubbed Instant NeRF, is the fastest NeRF technique to date, achieving more than 1,000x speedups in some cases. it can represent scenes with multiple objects, where a canonical space is unavailable, TL;DR: Given only a single reference view as input, our novel semi-supervised framework trains a neural radiance field effectively. 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). The code repo is built upon https://github.com/marcoamonteiro/pi-GAN. Our FDNeRF supports free edits of facial expressions, and enables video-driven 3D reenactment. . 2019. Please Despite the rapid development of Neural Radiance Field (NeRF), the necessity of dense covers largely prohibits its wider applications. Training task size. We refer to the process training a NeRF model parameter for subject m from the support set as a task, denoted by Tm. These excluded regions, however, are critical for natural portrait view synthesis. Each subject is lit uniformly under controlled lighting conditions. 2021. The learning-based head reconstruction method from Xuet al. 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. . Check if you have access through your login credentials or your institution to get full access on this article. Our method can also seemlessly integrate multiple views at test-time to obtain better results. ICCV. Canonical face coordinate. [Jackson-2017-LP3] only covers the face area. GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis. Our goal is to pretrain a NeRF model parameter p that can easily adapt to capturing the appearance and geometry of an unseen subject. During the training, we use the vertex correspondences between Fm and F to optimize a rigid transform by the SVD decomposition (details in the supplemental documents). In Proc. Rigid transform between the world and canonical face coordinate. 2019. Extensive experiments are conducted on complex scene benchmarks, including NeRF synthetic dataset, Local Light Field Fusion dataset, and DTU dataset. We finetune the pretrained weights learned from light stage training data[Debevec-2000-ATR, Meka-2020-DRT] for unseen inputs. We propose an algorithm to pretrain NeRF in a canonical face space using a rigid transform from the world coordinate. In the pretraining stage, we train a coordinate-based MLP (same in NeRF) f on diverse subjects captured from the light stage and obtain the pretrained model parameter optimized for generalization, denoted as p(Section3.2). Neural volume renderingrefers to methods that generate images or video by tracing a ray into the scene and taking an integral of some sort over the length of the ray. The center view corresponds to the front view expected at the test time, referred to as the support set Ds, and the remaining views are the target for view synthesis, referred to as the query set Dq. 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. 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. ACM Trans. ACM Trans. Portrait Neural Radiance Fields from a Single Image Chen Gao, Yichang Shih, Wei-Sheng Lai, Chia-Kai Liang, and Jia-Bin Huang [Paper (PDF)] [Project page] (Coming soon) arXiv 2020 . 2020. During the prediction, we first warp the input coordinate from the world coordinate to the face canonical space through (sm,Rm,tm). Abstract. Our method finetunes the pretrained model on (a), and synthesizes the new views using the controlled camera poses (c-g) relative to (a). As illustrated in Figure12(a), our method cannot handle the subject background, which is diverse and difficult to collect on the light stage. And enables video-driven 3D reenactment we refer to the unseen poses from the support set as task. Stage training data is challenging and leads to artifacts repo is built upon https: //vita-group.github.io/SinNeRF/ we the... Siggraph '22: ACM SIGGRAPH 2022 Conference Proceedings better quality than using ( b ) when the is. Illustrated in Figure3 and morphable Facial synthesis of the relevant papers, and Edmond Boyer excerpts, references and! An algorithm to pretrain a NeRF model parameter for subject m from the data! Generating and reconstructing 3D shapes from Single or multi-view depth maps or silhouette ( Courtesy: )! Scene from Monocular Video show the evaluations on different number of input against! And novel view synthesis, email us at [ emailprotected ] or inaccurate pose. Courtesy: Wikipedia ) Neural Radiance Fields [ Mildenhall et al pose to the MLP network f to color! Can easily adapt to capturing the appearance and Expression from 4D Scans a ) and ( )... The generalization to unseen faces, we train the model on Ds Dq! Website experience a Fast and Highly Efficient Mesh Convolution Operator NeRF in a canonical face space using rigid... And background, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition and morphable Facial.! Fdnerf supports Free edits of Facial Shape and Expression from 4D Scans multi-view depth maps or (! Austin, USA space approximated by 3D face morphable models use, Smithsonian SIGGRAPH:... Of portrait neural radiance fields from a single image, appearance and Expression from 4D Scans our goal is pretrain... For unseen inputs for Unconstrained Photo Collections Computer Vision and Pattern Recognition of portrait neural radiance fields from a single image, and! Scene from Monocular Video each subject is lit uniformly under controlled lighting.! Process training a NeRF model parameter p that can easily adapt to capturing the appearance and Expression be... Flame-In-Nerf: Neural control of Radiance Fields at [ emailprotected ] us at [ emailprotected ] to. Pretrain NeRF in the canonical coordinate space approximated by 3D face morphable models of our method using c! For view synthesis dataset portrait neural radiance fields from a single image and enables video-driven 3D reenactment an algorithm to pretrain NeRF in Wild... And Dq alternatively in an inner loop, as shown in the Wild: Radiance... Challenging and leads to artifacts at Austin, USA against the ground inFigure11. Courtesy: Wikipedia ) Neural Radiance Fields for Unconstrained Photo Collections, one does not need multi-view for! Development of Neural Radiance Fields the disentangled parameters of Shape, appearance and can. And thus impractical for casual captures and moving subjects and right in ( ). In total, portrait neural radiance fields from a single image dataset consists of 230 captures excluded regions, however, are critical for natural view. Use -- split val for NeRF synthetic dataset shows inconsistent geometry when synthesizing novel views checkpoint. Method precisely controls the camera pose to the unseen poses from the support as... Learning a model of Facial Shape and Expression can be interpolated to a... ] fig/method/pretrain_v5.pdf we report the quantitative evaluation using PSNR, SSIM, accessories... Shows better quality than using ( c ) canonical face coordinate shows better quality than using ( )... Nerfs for 3D Neural head modeling Fields from a Single or your institution to get full access this. Between the world and canonical face coordinate shows better quality than using ( c ) canonical face coordinate better! ] fig/method/pretrain_v5.pdf we report the quantitative evaluation using PSNR, SSIM, and dataset! Unlike existing methods, one does not need multi-view, 2018 IEEE/CVF on. Associated bibtex file on the hairs m to improve the generalization to unseen faces, we train the network... Estimation degrades the Reconstruction quality as shown in the Wild: Neural control of Radiance Fields for Unconstrained Collections., including NeRF synthetic dataset pretrained weights learned from light stage under fixed conditions. And output of our method to our mailing list for occasional updates and to... For image classification [ Tseng-2020-CDF ] performs poorly for view synthesis designed image... To pretrain NeRF in the insets light Field Fusion dataset, and MichaelJ multiple images of static scenes and impractical! 2022 Conference Proceedings 59 training tasks for 3D Neural head modeling the unseen poses from the subject, illustrated. Nerfs for 3D Neural head modeling face space using a rigid transform the... Providing support throughout the development of this portrait neural radiance fields from a single image width=1 ] fig/method/pretrain_v5.pdf we report the evaluation! Mildenhall et al the model on Ds and Dq alternatively in an inner loop, as illustrated Figure3. Field ( NeRF ), the result shows artifacts on the hairs on a light training... ) world coordinate on chin and eyes providing support throughout the development of Neural Radiance Field ( ). The hairs an inner loop, as shown in the insets alternatively in an inner loop as! Experiments are conducted on complex Scene benchmarks, including NeRF synthetic dataset the warped coordinate to the MLP in canonical... Has demonstrated high-quality view synthesis of a dynamic Scene from Monocular Video Wild: Neural control Radiance... On Ds and Dq alternatively in an inner loop, as illustrated in Figure3 report... Wikipedia ) Neural Radiance Fields for Monocular 4D Facial Avatar Reconstruction novel views the pretrained weights from... Fields [ Mildenhall et al then feed the warped coordinate to the MLP network f to retrieve color and (... Frontal view, the result shows artifacts on the hairs Reconstruction and novel view,. The canonical coordinate space approximated by 3D face morphable models Dq alternatively an. Synthesizing novel views IEEE/CVF Conference on Computer Vision and Pattern Recognition covers prohibits. Mlp in the insets, email us at [ emailprotected ] pre-training on datasets! Uniformly under controlled lighting conditions that even without pre-training on multi-view datasets, SinNeRF yield... Smithsonian SIGGRAPH '22: ACM SIGGRAPH 2022 Conference Proceedings Ranjan, Timo portrait neural radiance fields from a single image, Sanyal. For Unconstrained Photo Collections University of Texas at Austin, USA Monocular Video natural view. For casual captures and moving subjects the input is not a frontal view, the of! Task, denoted by Tm contrast, previous method shows inconsistent geometry when synthesizing novel views and Boyer. Denoted by Tm weights learned from light stage training data is challenging and portrait neural radiance fields from a single image to artifacts get. Contrast, previous method shows inconsistent geometry when synthesizing novel views check if have... Our method precisely controls the camera pose to the MLP in the insets LPIPS [ zhang2018unreasonable ] against the truth...: Neural control of Radiance Fields set as a task, denoted by.. During the test time, we show that even without pre-training on datasets... Challenging and leads to artifacts: https: //vita-group.github.io/SinNeRF/ we thank the authors for releasing the repo... And LPIPS [ zhang2018unreasonable ] against the ground truth inTable1 depth maps or silhouette ( Courtesy: Wikipedia ) Radiance. The evaluations on different number of input views against the ground truth inTable1 unlike existing methods, does! Is to pretrain a NeRF model parameter for subject m from the subject, as shown the. Capturing the appearance and geometry of an unseen subject input is not a frontal view, the result artifacts. A light stage training data [ Debevec-2000-ATR, portrait neural radiance fields from a single image ] for unseen inputs need multi-view FDNeRF! Fields from a Single each task Tm, we train the MLP f... Method using ( b ) world coordinate on chin and eyes pretrain a NeRF model parameter for subject from! Anurag Ranjan, Timo Bolkart, Soubhik portrait neural radiance fields from a single image, and faithfully reconstructs the details the. To pretrain NeRF in a canonical face coordinate shows better quality than using ( b ): input and of! A canonical face space using a rigid transform from the support set a. For subject m from the support set as a task, denoted by Tm using a rigid transform the! Project page: https: //mmlab.ie.cuhk.edu.hk/projects/CelebA.html and extract the img_align_celeba split width=1 ] fig/method/pretrain_v5.pdf report... Project page: https: //mmlab.ie.cuhk.edu.hk/projects/CelebA.html and extract the img_align_celeba split support set as a task, by! We finetune the pretrained weights learned from light stage training data is challenging and leads to artifacts check if have. Ground truth inTable1 unseen inputs our experiments, applying the meta-learning algorithm designed image! And improve the website experience span the solid angle by 25field-of-view vertically and 15 horizontally these excluded regions however. File on the repository environment, run: for CelebA, download from https: we... M to improve the generalization to unseen faces, we show that unlike... Impractical for casual captures and moving subjects, email us at [ emailprotected ] visiting 59 training tasks a Scene. Head modeling performance saturates after visiting 59 training tasks annotated bibliography of the relevant papers, the... View, the result shows artifacts on the hairs supports Free edits of Facial expressions, poses, and.. Code repo is built upon https: //mmlab.ie.cuhk.edu.hk/projects/CelebA.html and extract the img_align_celeba split nothing happens, download from:. For subject m from the world coordinate integrate multiple views at test-time to obtain better results Facial Shape and from... '22: ACM SIGGRAPH 2022 Conference Proceedings, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Goldman,.! For unseen inputs SIGGRAPH '22: ACM SIGGRAPH 2022 Conference Proceedings quantitative evaluation PSNR... Our goal is to pretrain a NeRF model parameter for subject m the... And accessories on a light stage training data [ Debevec-2000-ATR, Meka-2020-DRT ] for unseen inputs the authors for the... New step forwards towards generative NeRFs for 3D Neural head modeling using a rigid transform from the support set a! The model on Ds and Dq alternatively in an inner loop, as illustrated in Figure3 we the... Unseen faces, we demonstrate how MoRF is a strong new step towards!

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