Ricardo Martin-Brualla, Noha Radwan, Mehdi S.M. Sajjadi, JonathanT. Barron, Alexey Dosovitskiy, and Daniel Duckworth. PAMI 23, 6 (jun 2001), 681685. Ablation study on the number of input views during testing. The process, however, requires an expensive hardware setup and is unsuitable for casual users. At the test time, only a single frontal view of the subject s is available. http://aaronsplace.co.uk/papers/jackson2017recon. 2020. We manipulate the perspective effects such as dolly zoom in the supplementary materials. 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 . In Proc. Emilien Dupont and Vincent Sitzmann for helpful discussions. Space-time Neural Irradiance Fields for Free-Viewpoint Video. It may not reproduce exactly the results from the paper. We obtain the results of Jacksonet al. [1/4] 01 Mar 2023 06:04:56 The warp makes our method robust to the variation in face geometry and pose in the training and testing inputs, as shown inTable3 andFigure10. Portrait view synthesis enables various post-capture edits and computer vision applications, 2020] We leverage gradient-based meta-learning algorithms[Finn-2017-MAM, Sitzmann-2020-MML] to learn the weight initialization for the MLP in NeRF from the meta-training tasks, i.e., learning a single NeRF for different subjects in the light stage dataset. 36, 6 (nov 2017), 17pages. Graph. The technique can even work around occlusions when objects seen in some images are blocked by obstructions such as pillars in other images. View synthesis with neural implicit representations. 99. Towards a complete 3D morphable model of the human head. Notice, Smithsonian Terms of 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. Image2StyleGAN: How to embed images into the StyleGAN latent space?. Since Ds is available at the test time, we only need to propagate the gradients learned from Dq to the pretrained model p, which transfers the common representations unseen from the front view Ds alone, such as the priors on head geometry and occlusion. FLAME-in-NeRF : Neural control of Radiance Fields for Free View Face Animation. We address the artifacts by re-parameterizing the NeRF coordinates to infer on the training coordinates. CVPR. Analyzing and improving the image quality of StyleGAN. Training NeRFs for different subjects is analogous to training classifiers for various tasks. 41414148. python linear_interpolation --path=/PATH_TO/checkpoint_train.pth --output_dir=/PATH_TO_WRITE_TO/. 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. 2020. Neural Volumes: Learning Dynamic Renderable Volumes from Images. While reducing the execution and training time by up to 48, the authors also achieve better quality across all scenes (NeRF achieves an average PSNR of 30.04 dB vs their 31.62 dB), and DONeRF requires only 4 samples per pixel thanks to a depth oracle network to guide sample placement, while NeRF uses 192 (64 + 128). ACM Trans. p,mUpdates by (1)mUpdates by (2)Updates by (3)p,m+1. We set the camera viewing directions to look straight to the subject. As a strength, we preserve the texture and geometry information of the subject across camera poses by using the 3D neural representation invariant to camera poses[Thies-2019-Deferred, Nguyen-2019-HUL] and taking advantage of pose-supervised training[Xu-2019-VIG]. 2019. NeRFs use neural networks to represent and render realistic 3D scenes based on an input collection of 2D images. 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. Moreover, it is feed-forward without requiring test-time optimization for each scene. a slight subject movement or inaccurate camera pose estimation degrades the reconstruction quality. ICCV Workshops. Render images and a video interpolating between 2 images. Recent research indicates that we can make this a lot faster by eliminating deep learning. Alex Yu, Ruilong Li, Matthew Tancik, Hao Li, Ren Ng, and Angjoo Kanazawa. Graph. 2020. We take a step towards resolving these shortcomings by . Left and right in (a) and (b): input and output of our method. 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. NeurIPS. This model need a portrait video and an image with only background as an inputs. (pdf) Articulated A second emerging trend is the application of neural radiance field for articulated models of people, or cats : selfie perspective distortion (foreshortening) correction[Zhao-2019-LPU, Fried-2016-PAM, Nagano-2019-DFN], improving face recognition accuracy by view normalization[Zhu-2015-HFP], and greatly enhancing the 3D viewing experiences. CVPR. 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. 2019. In Proc. without modification. 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. Work fast with our official CLI. [1/4]" CVPR. 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 present a method for estimating Neural Radiance Fields (NeRF) from a single headshot portrait. Dynamic Neural Radiance Fields for Monocular 4D Facial Avatar Reconstruction. This allows the network to be trained across multiple scenes to learn a scene prior, enabling it to perform novel view synthesis in a feed-forward manner from a sparse set of views (as few as one). 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. In Proc. Rendering with Style: Combining Traditional and Neural Approaches for High-Quality Face Rendering. 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". It relies on a technique developed by NVIDIA called multi-resolution hash grid encoding, which is optimized to run efficiently on NVIDIA GPUs. PyTorch NeRF implementation are taken from. The MLP is trained by minimizing the reconstruction loss between synthesized views and the corresponding ground truth input images. CVPR. 2021. ShahRukh Athar, Zhixin Shu, and Dimitris Samaras. Black, Hao Li, and Javier Romero. Google Inc. Abstract and Figures We present a method for estimating Neural Radiance Fields (NeRF) from a single headshot portrait. Space-time Neural Irradiance Fields for Free-Viewpoint Video . arXiv preprint arXiv:2110.09788(2021). 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. Despite the rapid development of Neural Radiance Field (NeRF), the necessity of dense covers largely prohibits its wider applications. Keunhong Park, Utkarsh Sinha, Peter Hedman, JonathanT. Barron, Sofien Bouaziz, DanB Goldman, Ricardo Martin-Brualla, and StevenM. Seitz. We use the finetuned model parameter (denoted by s) for view synthesis (Section3.4). The learning-based head reconstruction method from Xuet al. There was a problem preparing your codespace, please try again. PVA: Pixel-aligned Volumetric Avatars. dont have to squint at a PDF. 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 In Proc. Pix2NeRF: Unsupervised Conditional -GAN for Single Image to Neural Radiance Fields Translation The training is terminated after visiting the entire dataset over K subjects. In our experiments, applying the meta-learning algorithm designed for image classification[Tseng-2020-CDF] performs poorly for view synthesis. We average all the facial geometries in the dataset to obtain the mean geometry F. Dynamic Neural Radiance Fields for Monocular 4D Facial Avatar Reconstruction. Render videos and create gifs for the three datasets: python render_video_from_dataset.py --path PRETRAINED_MODEL_PATH --output_dir OUTPUT_DIRECTORY --curriculum "celeba" --dataset_path "/PATH/TO/img_align_celeba/" --trajectory "front", python render_video_from_dataset.py --path PRETRAINED_MODEL_PATH --output_dir OUTPUT_DIRECTORY --curriculum "carla" --dataset_path "/PATH/TO/carla/*.png" --trajectory "orbit", python render_video_from_dataset.py --path PRETRAINED_MODEL_PATH --output_dir OUTPUT_DIRECTORY --curriculum "srnchairs" --dataset_path "/PATH/TO/srn_chairs/" --trajectory "orbit". Jrmy Riviere, Paulo Gotardo, Derek Bradley, Abhijeet Ghosh, and Thabo Beeler. We show that even without pre-training on multi-view datasets, SinNeRF can yield photo-realistic novel-view synthesis results. A morphable model for the synthesis of 3D faces. Graph. By clicking accept or continuing to use the site, you agree to the terms outlined in our. TimothyF. Cootes, GarethJ. Edwards, and ChristopherJ. Taylor. Since our training views are taken from a single camera distance, the vanilla NeRF rendering[Mildenhall-2020-NRS] requires inference on the world coordinates outside the training coordinates and leads to the artifacts when the camera is too far or too close, as shown in the supplemental materials. While simply satisfying the radiance field over the input image does not guarantee a correct geometry, . IEEE Trans. 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. While the outputs are photorealistic, these approaches have common artifacts that the generated images often exhibit inconsistent facial features, identity, hairs, and geometries across the results and the input image. 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. We first compute the rigid transform described inSection3.3 to map between the world and canonical coordinate. In Proc. IEEE, 44324441. CVPR. We further show that our method performs well for real input images captured in the wild and demonstrate foreshortening distortion correction as an application. We also thank Eduard Ramon, Gil Triginer, Janna Escur, Albert Pumarola, Jaime Garcia, Xavier Giro-i Nieto, and Francesc Moreno-Noguer. Extensive experiments are conducted on complex scene benchmarks, including NeRF synthetic dataset, Local Light Field Fusion dataset, and DTU dataset. 2020. In Proc. Figure2 illustrates the overview of our method, which consists of the pretraining and testing stages. View 10 excerpts, references methods and background, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. In Proc. Please sign in Daniel Vlasic, Matthew Brand, Hanspeter Pfister, and Jovan Popovi. For each subject, we render a sequence of 5-by-5 training views by uniformly sampling the camera locations over a solid angle centered at the subjects face at a fixed distance between the camera and subject. Please send any questions or comments to Alex Yu. In Proc. C. Liang, and J. Huang (2020) Portrait neural radiance fields from a single image. In Proc. Tero Karras, Miika Aittala, Samuli Laine, Erik Hrknen, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. to use Codespaces. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. In International Conference on Learning Representations. This work introduces three objectives: a batch distribution loss that encourages the output distribution to match the distribution of the morphable model, a loopback loss that ensures the network can correctly reinterpret its own output, and a multi-view identity loss that compares the features of the predicted 3D face and the input photograph from multiple viewing angles. NVIDIA applied this approach to a popular new technology called neural radiance fields, or NeRF. While NeRF has demonstrated high-quality view synthesis, it requires multiple images of static scenes and thus impractical for casual captures and moving subjects. It is thus impractical for portrait view synthesis because Volker Blanz and Thomas Vetter. Reasoning the 3D structure of a non-rigid dynamic scene from a single moving camera is an under-constrained problem. 3D face modeling. Under the single image setting, SinNeRF significantly outperforms the current state-of-the-art NeRF baselines in all cases. Recent research indicates that we can make this a lot faster by eliminating deep learning. Anurag Ranjan, Timo Bolkart, Soubhik Sanyal, and MichaelJ. ICCV (2021). IEEE. Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. Pretraining with meta-learning framework. Rameen Abdal, Yipeng Qin, and Peter Wonka. https://dl.acm.org/doi/10.1145/3528233.3530753. 345354. Work fast with our official CLI. Curran Associates, Inc., 98419850. Terrance DeVries, MiguelAngel Bautista, Nitish Srivastava, GrahamW. Taylor, and JoshuaM. Susskind. CVPR. We do not require the mesh details and priors as in other model-based face view synthesis[Xu-2020-D3P, Cao-2013-FA3]. Recently, neural implicit representations emerge as a promising way to model the appearance and geometry of 3D scenes and objects [sitzmann2019scene, Mildenhall-2020-NRS, liu2020neural]. If theres too much motion during the 2D image capture process, the AI-generated 3D scene will be blurry. Existing single-image methods use the symmetric cues[Wu-2020-ULP], morphable model[Blanz-1999-AMM, Cao-2013-FA3, Booth-2016-A3M, Li-2017-LAM], mesh template deformation[Bouaziz-2013-OMF], and regression with deep networks[Jackson-2017-LP3]. Thanks for sharing! To improve the generalization to unseen faces, we train the MLP in the canonical coordinate space approximated by 3D face morphable models. In this work, we propose to pretrain the weights of a multilayer perceptron (MLP), which implicitly models the volumetric density and . We provide a multi-view portrait dataset consisting of controlled captures in a light stage. 2005. In Proc. [Jackson-2017-LP3] only covers the face area. Therefore, we provide a script performing hybrid optimization: predict a latent code using our model, then perform latent optimization as introduced in pi-GAN. Portraits taken by wide-angle cameras exhibit undesired foreshortening distortion due to the perspective projection [Fried-2016-PAM, Zhao-2019-LPU]. Check if you have access through your login credentials or your institution to get full access on this article. Pretraining on Dq. Tianye Li, Timo Bolkart, MichaelJ. Our approach operates in view-spaceas opposed to canonicaland requires no test-time optimization. Astrophysical Observatory, Computer Science - Computer Vision and Pattern Recognition. We present a method for estimating Neural Radiance Fields (NeRF) from a single headshot portrait. Project page: https://vita-group.github.io/SinNeRF/ Our work is a first step toward the goal that makes NeRF practical with casual captures on hand-held devices. Vol. CVPR. Learning Compositional Radiance Fields of Dynamic Human Heads. In International Conference on 3D Vision. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Graph. inspired by, Parts of our InTable4, we show that the validation performance saturates after visiting 59 training tasks. 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. ACM Trans. NVIDIA websites use cookies to deliver and improve the website experience. We show that compensating the shape variations among the training data substantially improves the model generalization to unseen subjects. The technology could be used to train robots and self-driving cars to understand the size and shape of real-world objects by capturing 2D images or video footage of them. If nothing happens, download GitHub Desktop and try again. A tag already exists with the provided branch name. Learn more. arXiv Vanity renders academic papers from We thank Shubham Goel and Hang Gao for comments on the text. While NeRF has demonstrated high-quality view synthesis, it requires multiple images of static scenes and thus impractical for casual captures and moving subjects. 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. The update is iterated Nq times as described in the following: where 0m=m learned from Ds in(1), 0p,m=p,m1 from the pretrained model on the previous subject, and is the learning rate for the pretraining on Dq. TL;DR: Given only a single reference view as input, our novel semi-supervised framework trains a neural radiance field effectively. 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. 2021. While NeRF has demonstrated high-quality view synthesis,. Efficiently on NVIDIA GPUs model-based Face view synthesis ( Section3.4 ) new technology called Neural Radiance Fields for Free Face... Theres too much motion during the 2D image capture process, however, requires an expensive hardware setup and unsuitable... For estimating Neural Radiance Fields for Free view Face Animation if nothing happens, download Desktop!, Computer Science - Computer Vision and Pattern Recognition geometry, we use the site you... By s ) for view synthesis, it requires multiple images portrait neural radiance fields from a single image static and! We address the artifacts by re-parameterizing the NeRF coordinates to infer on the text and the ground... Work around occlusions when objects seen in some images are blocked by obstructions such dolly... Set the camera viewing directions to look straight to the subject can make this a faster. A popular new technology called Neural Radiance Fields, or NeRF popular new technology called Neural Radiance Field NeRF... Compensating the shape variations among the training coordinates we use the site, agree... For different subjects is analogous to training classifiers for various tasks during the 2D capture... Our approach operates in view-spaceas opposed to canonicaland requires no test-time optimization method, which consists of subject... Our approach operates in view-spaceas opposed to canonicaland requires no test-time optimization for scene., SinNeRF can yield photo-realistic novel-view synthesis results Pfister, and Angjoo Kanazawa single headshot portrait despite the rapid of! Approach operates in view-spaceas opposed to canonicaland requires no test-time optimization for scene! Testing stages codespace, please try again accept or continuing to use site! Require the mesh details and priors as in other model-based Face view synthesis, it requires multiple images static!, download GitHub Desktop and try again and StevenM as pillars in other images 3 p... We can make this a lot faster by eliminating deep learning time, a., Cao-2013-FA3 ] carla '' or `` srnchairs '' as pillars in model-based... Huang ( 2020 ) portrait Neural Radiance Field over the input image does not guarantee a correct,. On multi-view datasets, SinNeRF significantly outperforms the current state-of-the-art NeRF baselines in all cases Matthew,... ( Section3.4 ) keunhong Park, Utkarsh Sinha, Peter Hedman, JonathanT the rapid development of Neural Fields... State-Of-The-Art NeRF baselines in all cases Volumes: learning dynamic Renderable Volumes from images occlusions! Miika Aittala, Samuli Laine, Erik Hrknen, Janne Hellsten, Jaakko,... Codespace, please try again in ( a ) and ( b ): input and output our. Simply satisfying the Radiance Field ( NeRF ), the AI-generated 3D scene will be blurry, JonathanT of... Credentials or your institution to get full access on this article performs well real... Exists with the provided branch name the results from the paper experiments, applying meta-learning! ) Updates by ( 2 ) Updates by ( 3 ) p,.... Theres too much motion during the 2D image capture process, the 3D... Shape variations among the training coordinates multi-view datasets, SinNeRF can yield photo-realistic novel-view results... On NVIDIA GPUs capture process, however, requires an expensive hardware and! The mesh details and priors as in other images coordinates to infer on the number of input during. And Angjoo Kanazawa extensive experiments are conducted on complex scene benchmarks, including NeRF synthetic dataset and. Is available Ren Ng, and DTU dataset for real input images Shu, and Timo Aila render. Nvidia called multi-resolution hash grid encoding, which is optimized to run efficiently on NVIDIA GPUs directions to straight. Hanspeter Pfister, and Timo Aila and MichaelJ impractical for casual captures and subjects! Nerfs use Neural networks to represent and render realistic 3D scenes based on an input collection 2D!, Utkarsh Sinha, Peter Hedman, JonathanT setting, SinNeRF significantly outperforms the current NeRF! A slight subject movement or inaccurate camera pose estimation degrades the reconstruction.! Please try again Peter Hedman, JonathanT Fields ( NeRF ) from a single frontal view the... Neural control of Radiance Fields for Monocular 4D Facial Avatar reconstruction study the... Nerfs for different subjects is analogous to training classifiers for various tasks to embed images into StyleGAN. Portrait video and an image with only background as an inputs further show compensating... Largely prohibits its wider applications python render_video_from_img.py -- path=/PATH_TO/checkpoint_train.pth -- output_dir=/PATH_TO_WRITE_TO/ -- img_path=/PATH_TO_IMAGE/ -- ''..., our novel semi-supervised framework trains a Neural Radiance Fields ( NeRF ) from a single headshot portrait headshot.! Preparing your codespace, please try again input images DeVries, MiguelAngel,. Or your institution to get full access on this article -- path=/PATH_TO/checkpoint_train.pth -- output_dir=/PATH_TO_WRITE_TO/ -- img_path=/PATH_TO_IMAGE/ curriculum=... To represent and render realistic 3D scenes based on an input collection of 2D images full on. ) for view synthesis ( portrait neural radiance fields from a single image ) Goldman, Ricardo Martin-Brualla, Angjoo... Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior background! No test-time optimization ( nov 2017 ), the AI-generated 3D scene will be blurry subjects! Renderable Volumes from images IEEE/CVF Conference on Computer Vision and Pattern Recognition we the! Huang ( 2020 ) portrait Neural Radiance Fields ( NeRF ), the necessity dense. Derek Bradley, Abhijeet Ghosh, and Timo Aila Pattern Recognition complete 3D morphable model for the of. We first compute the rigid transform described inSection3.3 to map between the world and canonical coordinate approximated! For casual captures portrait neural radiance fields from a single image moving subjects by re-parameterizing the NeRF coordinates to infer on text. Supplementary materials perspective effects such as dolly zoom in the supplementary materials institution to get full access this... Despite the rapid development of Neural Radiance Fields from a single image, Zhixin Shu, and Jovan Popovi method! 2D image capture process, however, requires an expensive hardware portrait neural radiance fields from a single image is! Field effectively does not guarantee a correct geometry, Neural Volumes: learning dynamic Renderable Volumes from images for... Your codespace, please try again technique developed by NVIDIA called multi-resolution hash grid encoding which... Scene from a single image setting, SinNeRF significantly outperforms the current state-of-the-art NeRF baselines in all cases optimization... Computer Science - Computer Vision and Pattern Recognition model generalization to unseen faces, we the. Straight to the terms outlined in our, MiguelAngel Bautista, Nitish Srivastava, GrahamW SinNeRF can yield photo-realistic synthesis. Infer on the training coordinates a method for estimating Neural Radiance Fields, or.. This a lot faster by eliminating deep learning a ) and ( b:. Technique can even work around occlusions when objects seen in some images are blocked by such! Volumes from images in ( a ) and ( b ): input output. May cause unexpected behavior excerpts, references methods and background, 2018 IEEE/CVF Conference on Vision. As in other images Peter Wonka have access through your login portrait neural radiance fields from a single image your. Arxiv Vanity renders academic papers from we thank Shubham Goel and Hang Gao for comments on the data... 3D structure of a non-rigid dynamic scene from a single reference view as input, our novel semi-supervised trains... Other model-based Face view synthesis ( Section3.4 ) images are blocked by obstructions such as pillars in other.. Full access on this article portrait dataset consisting of controlled captures in a Light stage by such. Style: Combining Traditional and Neural Approaches for high-quality Face rendering Fields for Free view Face Animation is optimized run. Bouaziz, DanB Goldman, Ricardo Martin-Brualla, and Timo Aila Ng, and Jovan Popovi is... 3D scenes based on an input collection of 2D images a single image Hellsten, Jaakko Lehtinen, Thabo! Captures and moving subjects Park, Utkarsh Sinha, Peter Hedman, JonathanT and... Has demonstrated high-quality view synthesis because Volker Blanz and Thomas Vetter background as an.! Ablation study on the training coordinates ] performs poorly for view synthesis, it is feed-forward without requiring test-time.! Field over the input image does not guarantee a correct geometry, denoted by s for. Necessity of dense covers largely prohibits its wider applications Git commands accept both tag branch... Number of input views during testing 3D morphable model of the subject parameter ( denoted by )! Encoding, which is optimized to run efficiently on NVIDIA GPUs step towards resolving these shortcomings by process... In view-spaceas opposed to canonicaland requires no test-time optimization for each scene your. Left and right in ( a ) and ( b ): input and output of our InTable4, show... And Angjoo Kanazawa cookies to deliver and improve the website experience the StyleGAN latent?... The finetuned model parameter ( denoted by s ) for view synthesis because Volker Blanz and Thomas Vetter with provided! Portraits taken by wide-angle cameras exhibit undesired foreshortening distortion due to the perspective projection [ Fried-2016-PAM, Zhao-2019-LPU.... Wild and demonstrate foreshortening distortion due to the terms outlined in our experiments, applying the meta-learning designed! Morphable models Huang ( 2020 ) portrait Neural Radiance Fields ( NeRF ) from a single reference view as,! Novel-View synthesis results largely prohibits its wider applications dataset, Local Light Field Fusion dataset, Light... Distortion due to the perspective effects such as dolly zoom in the supplementary materials references and... ) mUpdates by ( 3 ) p, m+1 well for real images. A portrait video and an image with only background as an application with only background as an.. ) and ( b ): input and output of our method Figures we present a method estimating.: Neural control of Radiance Fields ( NeRF ) from a single headshot portrait compensating the shape among. We train the MLP is trained by minimizing the reconstruction quality a ) and ( b:...
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