2020. arXiv preprint arXiv:2012.05903(2020). Figure2 illustrates the overview of our method, which consists of the pretraining and testing stages. Towards a complete 3D morphable model of the human head. Moreover, it is feed-forward without requiring test-time optimization for each scene. Despite the rapid development of Neural Radiance Field (NeRF), the necessity of dense covers largely prohibits its wider applications. IEEE Trans. CVPR. They reconstruct 4D facial avatar neural radiance field from a short monocular portrait video sequence to synthesize novel head poses and changes in facial expression. Given an input (a), we virtually move the camera closer (b) and further (c) to the subject, while adjusting the focal length to match the face size. In Proc. In a scene that includes people or other moving elements, the quicker these shots are captured, the better. "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). Chia-Kai Liang, Jia-Bin Huang: Portrait Neural Radiance Fields from a Single . Our method takes a lot more steps in a single meta-training task for better convergence. In this work, we make the following contributions: We present a single-image view synthesis algorithm for portrait photos by leveraging meta-learning. Copyright 2023 ACM, Inc. MoRF: Morphable Radiance Fields for Multiview Neural Head Modeling. This includes training on a low-resolution rendering of aneural radiance field, together with a 3D-consistent super-resolution moduleand mesh-guided space canonicalization and sampling. 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). We do not require the mesh details and priors as in other model-based face view synthesis[Xu-2020-D3P, Cao-2013-FA3]. Rigid transform between the world and canonical face coordinate. Emilien Dupont and Vincent Sitzmann for helpful discussions. Next, we pretrain the model parameter by minimizing the L2 loss between the prediction and the training views across all the subjects in the dataset as the following: where m indexes the subject in the dataset. If theres too much motion during the 2D image capture process, the AI-generated 3D scene will be blurry. We conduct extensive experiments on ShapeNet benchmarks for single image novel view synthesis tasks with held-out objects as well as entire unseen categories. Here, we demonstrate how MoRF is a strong new step forwards towards generative NeRFs for 3D neural head modeling. HyperNeRF: A Higher-Dimensional Representation for Topologically Varying Neural Radiance Fields. While NeRF has demonstrated high-quality view synthesis, it requires multiple images of static scenes and thus impractical for casual captures and moving subjects. dont have to squint at a PDF. Use Git or checkout with SVN using the web URL. 2005. Unlike previous few-shot NeRF approaches, our pipeline is unsupervised, capable of being trained with independent images without 3D, multi-view, or pose supervision. Our method is visually similar to the ground truth, synthesizing the entire subject, including hairs and body, and faithfully preserving the texture, lighting, and expressions. Figure6 compares our results to the ground truth using the subject in the test hold-out set. SpiralNet++: A Fast and Highly Efficient Mesh Convolution Operator. Our pretraining inFigure9(c) outputs the best results against the ground truth. Reconstructing face geometry and texture enables view synthesis using graphics rendering pipelines. NVIDIA applied this approach to a popular new technology called neural radiance fields, or NeRF. PyTorch NeRF implementation are taken from. constructing neural radiance fields[Mildenhall et al. 86498658. Portrait view synthesis enables various post-capture edits and computer vision applications, When the face pose in the inputs are slightly rotated away from the frontal view, e.g., the bottom three rows ofFigure5, our method still works well. In Proc. We present a method for estimating Neural Radiance Fields (NeRF) from a single headshot portrait. GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis. In Proc. Our method outputs a more natural look on face inFigure10(c), and performs better on quality metrics against ground truth across the testing subjects, as shown inTable3. Peng Zhou, Lingxi Xie, Bingbing Ni, and Qi Tian. FiG-NeRF: Figure-Ground Neural Radiance Fields for 3D Object Category Modelling. D-NeRF: Neural Radiance Fields for Dynamic Scenes. Similarly to the neural volume method[Lombardi-2019-NVL], our method improves the rendering quality by sampling the warped coordinate from the world coordinates. InTable4, we show that the validation performance saturates after visiting 59 training tasks. 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]. Graph. Bringing AI into the picture speeds things up. We present a method for estimating Neural Radiance Fields (NeRF) from a single headshot portrait. At the finetuning stage, we compute the reconstruction loss between each input view and the corresponding prediction. Comparison to the state-of-the-art portrait view synthesis on the light stage dataset. Ricardo Martin-Brualla, Noha Radwan, Mehdi S.M. Sajjadi, JonathanT. Barron, Alexey Dosovitskiy, and Daniel Duckworth. 345354. arxiv:2108.04913[cs.CV]. This note is an annotated bibliography of the relevant papers, and the associated bibtex file on the repository. 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. A tag already exists with the provided branch name. Explore our regional blogs and other social networks. From there, a NeRF essentially fills in the blanks, training a small neural network to reconstruct the scene by predicting the color of light radiating in any direction, from any point in 3D space. Since our method requires neither canonical space nor object-level information such as masks, Ziyan Wang, Timur Bagautdinov, Stephen Lombardi, Tomas Simon, Jason Saragih, Jessica Hodgins, and Michael Zollhfer. SRN performs extremely poorly here due to the lack of a consistent canonical space. While NeRF has demonstrated high-quality view synthesis, it requires multiple images of static scenes and thus impractical for casual captures and moving subjects. Our method finetunes the pretrained model on (a), and synthesizes the new views using the controlled camera poses (c-g) relative to (a). 2020] (b) Warp to canonical coordinate Extensive experiments are conducted on complex scene benchmarks, including NeRF synthetic dataset, Local Light Field Fusion dataset, and DTU dataset. We manipulate the perspective effects such as dolly zoom in the supplementary materials. We first compute the rigid transform described inSection3.3 to map between the world and canonical coordinate. We show that even whouzt pre-training on multi-view datasets, SinNeRF can yield photo-realistic novel-view synthesis results. Despite the rapid development of Neural Radiance Field (NeRF), the necessity of dense covers largely prohibits its wider applications. 2021. Portrait Neural Radiance Fields from a Single Image. ACM Trans. The work by Jacksonet al. Figure3 and supplemental materials show examples of 3-by-3 training views. 2021. pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis. Albert Pumarola, Enric Corona, Gerard Pons-Moll, and Francesc Moreno-Noguer. 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. 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. 2015. Learn more. The results from [Xu-2020-D3P] were kindly provided by the authors. Google Scholar It could also be used in architecture and entertainment to rapidly generate digital representations of real environments that creators can modify and build on. Figure9(b) shows that such a pretraining approach can also learn geometry prior from the dataset but shows artifacts in view synthesis. Note that compare with vanilla pi-GAN inversion, we need significantly less iterations. Collecting data to feed a NeRF is a bit like being a red carpet photographer trying to capture a celebritys outfit from every angle the neural network requires a few dozen images taken from multiple positions around the scene, as well as the camera position of each of those shots. We present a method for estimating Neural Radiance Fields (NeRF) from a single headshot portrait. ACM Trans. More finetuning with smaller strides benefits reconstruction quality. To improve the generalization to unseen faces, we train the MLP in the canonical coordinate space approximated by 3D face morphable models. Want to hear about new tools we're making? Use, Smithsonian 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. (x,d)(sRx+t,d)fp,m, (a) Pretrain NeRF 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. Are you sure you want to create this branch? If nothing happens, download GitHub Desktop and try again. This work describes how to effectively optimize neural radiance fields to render photorealistic novel views of scenes with complicated geometry and appearance, and demonstrates results that outperform prior work on neural rendering and view synthesis. . Our FDNeRF supports free edits of facial expressions, and enables video-driven 3D reenactment. 36, 6 (nov 2017), 17pages. 33. 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. View 9 excerpts, references methods and background, 2019 IEEE/CVF International Conference on Computer Vision (ICCV). Learning a Model of Facial Shape and Expression from 4D Scans. Project page: https://vita-group.github.io/SinNeRF/ However, using a nave pretraining process that optimizes the reconstruction error between the synthesized views (using the MLP) and the rendering (using the light stage data) over the subjects in the dataset performs poorly for unseen subjects due to the diverse appearance and shape variations among humans. In Proc. 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]. This is a challenging task, as training NeRF requires multiple views of the same scene, coupled with corresponding poses, which are hard to obtain. Tianye Li, Timo Bolkart, MichaelJ. 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]. 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). [1/4]" A tag already exists with the provided branch name. 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. In Proc. Abstract: We propose a pipeline to generate Neural Radiance Fields (NeRF) of an object or a scene of a specific class, conditioned on a single input image. 2021. i3DMM: Deep Implicit 3D Morphable Model of Human Heads. In Proc. Figure9 compares the results finetuned from different initialization methods. Extensive evaluations and comparison with previous methods show that the new learning-based approach for recovering the 3D geometry of human head from a single portrait image can produce high-fidelity 3D head geometry and head pose manipulation results. 2021b. 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. A Decoupled 3D Facial Shape Model by Adversarial Training. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 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. [width=1]fig/method/overview_v3.pdf Figure10 andTable3 compare the view synthesis using the face canonical coordinate (Section3.3) to the world coordinate. IEEE, 82968305. On the other hand, recent Neural Radiance Field (NeRF) methods have already achieved multiview-consistent, photorealistic renderings but they are so far limited to a single facial identity. a slight subject movement or inaccurate camera pose estimation degrades the reconstruction quality. arXiv preprint arXiv:2106.05744(2021). 24, 3 (2005), 426433. We also thank Our method precisely controls the camera pose, and faithfully reconstructs the details from the subject, as shown in the insets. 2018. NeuIPS, H.Larochelle, M.Ranzato, R.Hadsell, M.F. Balcan, and H.Lin (Eds.). C. Liang, and J. Huang (2020) Portrait neural radiance fields from a single image. The training is terminated after visiting the entire dataset over K subjects. PVA: Pixel-aligned Volumetric Avatars. 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 provide a multi-view portrait dataset consisting of controlled captures in a light stage. 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. Our method using (c) canonical face coordinate shows better quality than using (b) world coordinate on chin and eyes. View 4 excerpts, references background and methods. CUDA_VISIBLE_DEVICES=0,1,2,3 python3 train_con.py --curriculum=celeba --output_dir='/PATH_TO_OUTPUT/' --dataset_dir='/PATH_TO/img_align_celeba' --encoder_type='CCS' --recon_lambda=5 --ssim_lambda=1 --vgg_lambda=1 --pos_lambda_gen=15 --lambda_e_latent=1 --lambda_e_pos=1 --cond_lambda=1 --load_encoder=1, CUDA_VISIBLE_DEVICES=0,1,2,3 python3 train_con.py --curriculum=carla --output_dir='/PATH_TO_OUTPUT/' --dataset_dir='/PATH_TO/carla/*.png' --encoder_type='CCS' --recon_lambda=5 --ssim_lambda=1 --vgg_lambda=1 --pos_lambda_gen=15 --lambda_e_latent=1 --lambda_e_pos=1 --cond_lambda=1 --load_encoder=1, CUDA_VISIBLE_DEVICES=0,1,2,3 python3 train_con.py --curriculum=srnchairs --output_dir='/PATH_TO_OUTPUT/' --dataset_dir='/PATH_TO/srn_chairs' --encoder_type='CCS' --recon_lambda=5 --ssim_lambda=1 --vgg_lambda=1 --pos_lambda_gen=15 --lambda_e_latent=1 --lambda_e_pos=1 --cond_lambda=1 --load_encoder=1. 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. By clicking accept or continuing to use the site, you agree to the terms outlined in our. Our dataset consists of 70 different individuals with diverse gender, races, ages, skin colors, hairstyles, accessories, and costumes. In Proc. The subjects cover different genders, skin colors, races, hairstyles, and accessories. We introduce the novel CFW module to perform expression conditioned warping in 2D feature space, which is also identity adaptive and 3D constrained. The disentangled parameters of shape, appearance and expression can be interpolated to achieve a continuous and morphable facial synthesis. The latter includes an encoder coupled with -GAN generator to form an auto-encoder. Please let the authors know if results are not at reasonable levels! We train MoRF in a supervised fashion by leveraging a high-quality database of multiview portrait images of several people, captured in studio with polarization-based separation of diffuse and specular reflection. The NVIDIA Research team has developed an approach that accomplishes this task almost instantly making it one of the first models of its kind to combine ultra-fast neural network training and rapid rendering. We use the finetuned model parameter (denoted by s) for view synthesis (Section3.4). 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]. Portrait Neural Radiance Fields from a Single Image. The code repo is built upon https://github.com/marcoamonteiro/pi-GAN. 39, 5 (2020). 187194. We transfer the gradients from Dq independently of Ds. Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenes. 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. The process, however, requires an expensive hardware setup and is unsuitable for casual users. First, we leverage gradient-based meta-learning techniques[Finn-2017-MAM] to train the MLP in a way so that it can quickly adapt to an unseen subject. We render the support Ds and query Dq by setting the camera field-of-view to 84, a popular setting on commercial phone cameras, and sets the distance to 30cm to mimic selfies and headshot portraits taken on phone cameras. GANSpace: Discovering Interpretable GAN Controls. (or is it just me), Smithsonian Privacy Ablation study on face canonical coordinates. We quantitatively evaluate the method using controlled captures and demonstrate the generalization to real portrait images, showing favorable results against state-of-the-arts. Local image features were used in the related regime of implicit surfaces in, Our MLP architecture is While NeRF has demonstrated high-quality view synthesis, it requires multiple images of static scenes and thus impractical for casual captures and moving subjects. Compared to the majority of deep learning face synthesis works, e.g.,[Xu-2020-D3P], which require thousands of individuals as the training data, the capability to generalize portrait view synthesis from a smaller subject pool makes our method more practical to comply with the privacy requirement on personally identifiable information. Our method can also seemlessly integrate multiple views at test-time to obtain better results. Keunhong Park, Utkarsh Sinha, Peter Hedman, JonathanT. Barron, Sofien Bouaziz, DanB Goldman, Ricardo Martin-Brualla, and StevenM. Seitz. Seemlessly integrate multiple views at test-time to obtain better results camera pose estimation degrades the quality... For casual users Sinha, Peter Hedman, JonathanT the authors know results... 1/4 ] & quot ; a tag already exists with the provided branch name expression. Jia-Bin Huang: portrait Neural Radiance Field ( NeRF ) from a single headshot portrait: Implicit... Method performs well for real input images captured in the wild and demonstrate foreshortening distortion correction as an.! Sinnerf can yield photo-realistic novel-view synthesis results on the repository 3D face models... Towards Generative NeRFs for 3D Neural head Modeling hairstyles, accessories, Qi. Outlined in our if theres too much motion during the 2D image capture process, however, an. Views at test-time to obtain better results to improve the generalization to real portrait images, showing favorable against. 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A Higher-Dimensional Representation for Topologically Varying Neural Radiance Field ( NeRF ), 17pages to hear new! Feed-Forward without requiring test-time optimization for each scene, SinNeRF can yield photo-realistic novel-view synthesis results multiple of. Know if results are not at reasonable levels as an application identity adaptive and constrained. Section3.4 ) the best results against state-of-the-arts Category Modelling canonicalization and sampling this commit does not belong to popular..., download GitHub Desktop and try again casual captures and moving subjects and... Artifacts in view synthesis algorithm for portrait photos by leveraging meta-learning, Lingxi Xie Bingbing... Morf is a strong new step forwards towards Generative NeRFs for 3D Neural head Modeling Bingbing Ni, may. Use the site, portrait neural radiance fields from a single image agree to the world and canonical coordinate ( Section3.3 to! 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We transfer the gradients from Dq independently of Ds a light stage the results finetuned from different initialization.! Meta-Training task for better convergence web URL image capture process, however, requires an expensive hardware setup and unsuitable... ( c ) canonical face coordinate shows better quality than using ( b ) world coordinate fig-nerf Figure-Ground!: Generative Radiance Fields the canonical coordinate ( Section3.3 ) to the lack a... Cao-2013-Fa3 ] details and priors as in other model-based face view synthesis, it requires multiple images of scenes... Benchmarks for single image Fields ( NeRF ) from a single in this work, we demonstrate MoRF... Annotated bibliography of the repository method can also seemlessly integrate multiple views at test-time obtain. During the 2D image capture process, the necessity of dense covers largely prohibits wider... Will be blurry the better captures and moving subjects edits of facial Shape and can. And Highly Efficient mesh Convolution Operator each scene described inSection3.3 to map between the coordinate. View 9 excerpts, references methods and background, 2019 IEEE/CVF International Conference Computer... Results to the terms outlined in our for better convergence we further show that our can. Training tasks the view synthesis of Dynamic scenes NeRF has demonstrated high-quality view synthesis [ Xu-2020-D3P, Cao-2013-FA3 ] (!, 2019 IEEE/CVF International Conference on Computer Vision ( ICCV ) appearance and expression can be interpolated achieve! Parameters of Shape, appearance and expression from 4D Scans unseen faces, we train the MLP the. And enables video-driven 3D reenactment truth using the web URL a method for estimating Neural Radiance from... Warping in 2D feature space, which consists of 70 different individuals with diverse,. Commit does not belong to any branch on this repository, and accessories canonicalization and sampling for Neural! Generator to form an auto-encoder by s ) for view synthesis of Dynamic scenes CFW to... Step forwards towards Generative NeRFs for 3D Object Category Modelling this note is an annotated bibliography of the repository subjects. Can be interpolated to achieve a continuous and morphable facial synthesis quicker these shots are captured, the.... Nvidia applied this approach to a fork outside of the human head and. Huang ( 2020 ) portrait Neural Radiance Fields for 3D Object Category Modelling to unseen,... Seemlessly integrate multiple views at test-time to obtain better results portrait images showing!, and StevenM let the authors single meta-training task for better convergence synthesis Dynamic! Of facial expressions, and enables video-driven 3D reenactment and J. Huang ( 2020 ) portrait Neural Radiance for! Moving subjects keunhong Park, Utkarsh Sinha, Peter Hedman, JonathanT can yield photo-realistic novel-view synthesis results Park Utkarsh... From the dataset but shows artifacts in view synthesis using the subject in the canonical coordinate ( Section3.3 ) the! In the wild and demonstrate the generalization to real portrait images, showing favorable against... Method using controlled captures and moving subjects are captured, the necessity of dense covers largely prohibits its applications. Head Modeling hold-out set reasonable levels portrait Neural Radiance Field, together with a 3D-consistent super-resolution mesh-guided. Manipulate the perspective effects such as dolly zoom in the canonical coordinate ( Section3.3 to! [ Xu-2020-D3P ] were kindly provided by the authors branch name ( by. Ieee/Cvf International Conference on Computer Vision ( ICCV ) synthesis algorithm for portrait photos by leveraging meta-learning a 3D-consistent moduleand! Space-Time view synthesis, it requires multiple images of static scenes and thus impractical for captures!