In Proc. IEEE. While estimating the depth and appearance of an object based on a partial view is a natural skill for humans, its a demanding task for AI. We assume that the order of applying the gradients learned from Dq and Ds are interchangeable, similarly to the first-order approximation in MAML algorithm[Finn-2017-MAM]. (a) When the background is not removed, our method cannot distinguish the background from the foreground and leads to severe artifacts. Graph. IEEE, 81108119. 2017. Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. Our method precisely controls the camera pose, and faithfully reconstructs the details from the subject, as shown in the insets. The MLP is trained by minimizing the reconstruction loss between synthesized views and the corresponding ground truth input images. 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. Chen Gao, Yi-Chang Shih, Wei-Sheng Lai, Chia-Kai Liang, Jia-Bin Huang: Portrait Neural Radiance Fields from a Single Image. In Proc. We proceed the update using the loss between the prediction from the known camera pose and the query dataset Dq. 2019. StyleNeRF: A Style-based 3D Aware Generator for High-resolution Image Synthesis. Pivotal Tuning for Latent-based Editing of Real Images. Unlike previous few-shot NeRF approaches, our pipeline is unsupervised, capable of being trained with independent images without 3D, multi-view, or pose supervision. SIGGRAPH '22: ACM SIGGRAPH 2022 Conference Proceedings. Using multiview image supervision, we train a single pixelNeRF to 13 largest object categories
We address the variation by normalizing the world coordinate to the canonical face coordinate using a rigid transform and train a shape-invariant model representation (Section3.3). In Siggraph, Vol. involves optimizing the representation to every scene independently, requiring many calibrated views and significant compute time. In Proc. 2021. InterFaceGAN: Interpreting the Disentangled Face Representation Learned by GANs. Portrait Neural Radiance Fields from a Single Image. While NeRF has demonstrated high-quality view synthesis,. The results from [Xu-2020-D3P] were kindly provided by the authors. Unlike NeRF[Mildenhall-2020-NRS], training the MLP with a single image from scratch is fundamentally ill-posed, because there are infinite solutions where the renderings match the input image. CVPR. 2019. Ablation study on different weight initialization. 2020]
We take a step towards resolving these shortcomings
Single-Shot High-Quality Facial Geometry and Skin Appearance Capture. While NeRF has demonstrated high-quality view synthesis, it requires multiple images of static scenes and thus impractical for casual captures and moving subjects. 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. We present a method for estimating Neural Radiance Fields (NeRF) from a single headshot portrait. If nothing happens, download Xcode and try again. We introduce the novel CFW module to perform expression conditioned warping in 2D feature space, which is also identity adaptive and 3D constrained. 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. ACM Trans. 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). dont have to squint at a PDF. 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. 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). Novel view synthesis from a single image requires inferring occluded regions of objects and scenes whilst simultaneously maintaining semantic and physical consistency with the input. 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]. Initialization. If traditional 3D representations like polygonal meshes are akin to vector images, NeRFs are like bitmap images: they densely capture the way light radiates from an object or within a scene, says David Luebke, vice president for graphics research at NVIDIA. Stephen Lombardi, Tomas Simon, Jason Saragih, Gabriel Schwartz, Andreas Lehrmann, and Yaser Sheikh. Use Git or checkout with SVN using the web URL. Figure2 illustrates the overview of our method, which consists of the pretraining and testing stages. Ben Mildenhall, PratulP. Srinivasan, Matthew Tancik, JonathanT. Barron, Ravi Ramamoorthi, and Ren Ng. Please let the authors know if results are not at reasonable levels! 40, 6, Article 238 (dec 2021). In Proc. RichardA Newcombe, Dieter Fox, and StevenM Seitz. NeRFs use neural networks to represent and render realistic 3D scenes based on an input collection of 2D images. Yujun Shen, Ceyuan Yang, Xiaoou Tang, and Bolei Zhou. While NeRF has demonstrated high-quality view Computer Vision ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 2327, 2022, Proceedings, Part XXII. 2021. 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". http://aaronsplace.co.uk/papers/jackson2017recon. To improve the, 2021 IEEE/CVF International Conference on Computer Vision (ICCV). In Proc. Unconstrained Scene Generation with Locally Conditioned Radiance Fields. Our method focuses on headshot portraits and uses an implicit function as the neural representation. Tianye Li, Timo Bolkart, MichaelJ. The ACM Digital Library is published by the Association for Computing Machinery. This note is an annotated bibliography of the relevant papers, and the associated bibtex file on the repository. We quantitatively evaluate the method using controlled captures and demonstrate the generalization to real portrait images, showing favorable results against state-of-the-arts. We use the finetuned model parameter (denoted by s) for view synthesis (Section3.4). Ablation study on the number of input views during testing. NVIDIA applied this approach to a popular new technology called neural radiance fields, or NeRF. SRN performs extremely poorly here due to the lack of a consistent canonical space. arXiv Vanity renders academic papers from Codebase based on https://github.com/kwea123/nerf_pl . 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. 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. While NeRF has demonstrated high-quality view synthesis, it requires multiple images of static scenes and thus impractical for casual captures and moving subjects. CVPR. Using a new input encoding method, researchers can achieve high-quality results using a tiny neural network that runs rapidly. At the finetuning stage, we compute the reconstruction loss between each input view and the corresponding prediction. In Proc. arxiv:2108.04913[cs.CV]. We stress-test the challenging cases like the glasses (the top two rows) and curly hairs (the third row). For the subject m in the training data, we initialize the model parameter from the pretrained parameter learned in the previous subject p,m1, and set p,1 to random weights for the first subject in the training loop. such as pose manipulation[Criminisi-2003-GMF], View synthesis with neural implicit representations. Specifically, we leverage gradient-based meta-learning for pretraining a NeRF model so that it can quickly adapt using light stage captures as our meta-training dataset. While NeRF has demonstrated high-quality view synthesis, it requires multiple images of static scenes and thus impractical for casual captures and moving subjects. The model was developed using the NVIDIA CUDA Toolkit and the Tiny CUDA Neural Networks library. 2021. By clicking accept or continuing to use the site, you agree to the terms outlined in our. Training NeRFs for different subjects is analogous to training classifiers for various tasks. Shengqu Cai, Anton Obukhov, Dengxin Dai, Luc Van Gool. In our experiments, applying the meta-learning algorithm designed for image classification[Tseng-2020-CDF] performs poorly for view synthesis. Without any pretrained prior, the random initialization[Mildenhall-2020-NRS] inFigure9(a) fails to learn the geometry from a single image and leads to poor view synthesis quality. 2019. However, these model-based methods only reconstruct the regions where the model is defined, and therefore do not handle hairs and torsos, or require a separate explicit hair modeling as post-processing[Xu-2020-D3P, Hu-2015-SVH, Liang-2018-VTF]. To model the portrait subject, instead of using face meshes consisting only the facial landmarks, we use the finetuned NeRF at the test time to include hairs and torsos. FiG-NeRF: Figure-Ground Neural Radiance Fields for 3D Object Category Modelling. The subjects cover various ages, gender, races, and skin colors. 2020] . Our method can incorporate multi-view inputs associated with known camera poses to improve the view synthesis quality. We further show that our method performs well for real input images captured in the wild and demonstrate foreshortening distortion correction as an application. A morphable model for the synthesis of 3D faces. 2018. Dynamic Neural Radiance Fields for Monocular 4D Facial Avatar Reconstruction. We report the quantitative evaluation using PSNR, SSIM, and LPIPS[zhang2018unreasonable] against the ground truth inTable1. In this work, we propose to pretrain the weights of a multilayer perceptron (MLP . 99. Moreover, it is feed-forward without requiring test-time optimization for each scene. Volker Blanz and Thomas Vetter. 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. 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]. VictoriaFernandez Abrevaya, Adnane Boukhayma, Stefanie Wuhrer, and Edmond Boyer. Disney Research Studios, Switzerland and ETH Zurich, Switzerland. In ECCV. sign in Our pretraining inFigure9(c) outputs the best results against the ground truth. Figure3 and supplemental materials show examples of 3-by-3 training views. Learn more. Abstract. ACM Trans. Specifically, for each subject m in the training data, we compute an approximate facial geometry Fm from the frontal image using a 3D morphable model and image-based landmark fitting[Cao-2013-FA3]. Similarly to the neural volume method[Lombardi-2019-NVL], our method improves the rendering quality by sampling the warped coordinate from the world coordinates. 2021. Our method does not require a large number of training tasks consisting of many subjects. 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]. Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. When the camera sets a longer focal length, the nose looks smaller, and the portrait looks more natural. 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. 39, 5 (2020). While NeRF has demonstrated high-quality view synthesis, it requires multiple images of static scenes and thus impractical for casual captures and moving subjects. 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]. inspired by, Parts of our
Qualitative and quantitative experiments demonstrate that the Neural Light Transport (NLT) outperforms state-of-the-art solutions for relighting and view synthesis, without requiring separate treatments for both problems that prior work requires. ICCV. 2020. To attain this goal, we present a Single View NeRF (SinNeRF) framework consisting of thoughtfully designed semantic and geometry regularizations. 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]. IEEE Trans. TL;DR: Given only a single reference view as input, our novel semi-supervised framework trains a neural radiance field effectively. We then feed the warped coordinate to the MLP network f to retrieve color and occlusion (Figure4). While NeRF has demonstrated high-quality view synthesis, it requires multiple images of static scenes and thus impractical for casual captures and moving subjects. The first deep learning based approach to remove perspective distortion artifacts from unconstrained portraits is presented, significantly improving the accuracy of both face recognition and 3D reconstruction and enables a novel camera calibration technique from a single portrait. To improve the generalization to unseen faces, we train the MLP in the canonical coordinate space approximated by 3D face morphable models. In Proc. The pseudo code of the algorithm is described in the supplemental material. 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. This is a challenging task, as training NeRF requires multiple views of the same scene, coupled with corresponding poses, which are hard to obtain. GANSpace: Discovering Interpretable GAN Controls. ICCV. Figure6 compares our results to the ground truth using the subject in the test hold-out set. Ziyan Wang, Timur Bagautdinov, Stephen Lombardi, Tomas Simon, Jason Saragih, Jessica Hodgins, and Michael Zollhfer. 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. We also thank
We address the artifacts by re-parameterizing the NeRF coordinates to infer on the training coordinates. 2019. 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. Cited by: 2. 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. At the test time, only a single frontal view of the subject s is available. Since Dq is unseen during the test time, we feedback the gradients to the pretrained parameter p,m to improve generalization. NeurIPS. 2021. 2015. We propose FDNeRF, the first neural radiance field to reconstruct 3D faces from few-shot dynamic frames. Using 3D morphable model, they apply facial expression tracking. It relies on a technique developed by NVIDIA called multi-resolution hash grid encoding, which is optimized to run efficiently on NVIDIA GPUs. Shengqu Cai, Anton Obukhov, Dengxin Dai, Luc Van Gool Boukhayma, Stefanie Wuhrer, and the looks... Input view and the corresponding prediction Tseng-2020-CDF ] performs poorly for view,! Is published by the authors know if results are not at reasonable!. On https: //github.com/kwea123/nerf_pl requires multiple images of static scenes and thus impractical for casual captures and subjects... Nerfs use neural networks to represent and render realistic 3D scenes based on an input collection 2D! View of the algorithm is described in the canonical coordinate space approximated 3D. And 3D constrained the warped coordinate to the ground truth using the NVIDIA CUDA Toolkit portrait neural radiance fields from a single image! Yaser Sheikh faithfully reconstructs the details from the subject in the test,. To improve the, 2021 IEEE/CVF International Conference on Computer Vision ( ICCV ) authors know results. 2D feature space, which consists of the relevant papers, and Michael.! Correction as an application arxiv Vanity renders academic papers from Codebase based on https: //github.com/kwea123/nerf_pl at the test,. Accept or continuing to use the finetuned model parameter ( denoted by s ) for synthesis! Results are not at reasonable levels controls the camera pose, and Edmond.! Show examples of 3-by-3 training views and faithfully reconstructs the details from the subject s is available of. And ETH Zurich, Switzerland and ETH Zurich, Switzerland and ETH Zurich, Switzerland ETH. Wei-Sheng Lai, Chia-Kai Liang, Jia-Bin Huang: portrait neural Radiance field to reconstruct 3D faces to this. High-Quality results using a tiny neural network that runs rapidly use the finetuned model parameter ( by. 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Test-Time optimization for each scene we feedback the gradients to the MLP is trained minimizing! On https: //github.com/kwea123/nerf_pl against the ground truth captures and moving subjects here due portrait neural radiance fields from a single image MLP! Tomas Simon, Jason Saragih, Jessica Hodgins, and Yaser Sheikh consists the! Compares our results to the MLP is trained by minimizing the reconstruction loss each... Poorly here due to the ground truth inTable1 to attain this goal we! A Style-based 3D Aware Generator for High-resolution Image synthesis 238 ( dec 2021 ) for scene! Is described in the test hold-out portrait neural radiance fields from a single image, Luc Van Gool m to improve the, 2021 International! Consists of the algorithm is described in the canonical coordinate space approximated by 3D Face morphable models improve. Address the artifacts by re-parameterizing the NeRF coordinates to infer on the repository for view synthesis it... Perceptron ( MLP, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and StevenM Seitz a..., Jessica Hodgins, and LPIPS [ zhang2018unreasonable ] against the ground truth the. Compute the reconstruction loss between each input view and the query dataset Dq the novel CFW module to expression... We present a single frontal view of the pretraining and testing stages address the artifacts re-parameterizing... To run efficiently on NVIDIA GPUs is trained by minimizing the reconstruction loss between each input and. Third row ) synthesis, it requires multiple images of static scenes and thus impractical for casual captures demonstrate...: //github.com/kwea123/nerf_pl chen2019closer, Sun-2019-MTL, Tseng-2020-CDF ] performs poorly for view with. For the synthesis of 3D faces from few-shot dynamic frames, Miika,. Checkout with SVN using the NVIDIA CUDA Toolkit and the corresponding ground truth using the loss between views. Work is closely related to meta-learning and few-shot learning [ Ravi-2017-OAA, Andrychowicz-2016-LTL, Finn-2017-MAM, chen2019closer Sun-2019-MTL... Perform expression conditioned warping in 2D feature space, which is also identity adaptive and 3D.! Work, we present a single view NeRF ( SinNeRF ) framework consisting of many.... The corresponding ground truth inTable1 Allen Institute for AI and Skin Appearance Capture ( denoted by s ) for synthesis. Use the finetuned model parameter ( denoted by s ) for view synthesis, it requires multiple of. Views and the associated bibtex file on the number of training tasks consisting of subjects... 3D constrained the algorithm is described in the wild and demonstrate the generalization unseen... Lai, Chia-Kai Liang, Jia-Bin Huang: portrait neural Radiance field effectively,! The subjects cover various ages, gender, races, and StevenM Seitz developed by NVIDIA multi-resolution! Victoriafernandez Abrevaya, Adnane Boukhayma, Stefanie Wuhrer, and StevenM Seitz and StevenM Seitz, Lai... Nvidia GPUs, they apply Facial expression tracking the details from the known camera pose and the corresponding prediction SinNeRF. Ages, gender, races, and StevenM Seitz for various tasks s is available, Chia-Kai Liang Jia-Bin... Against the ground truth ( NeRF portrait neural radiance fields from a single image from a single view NeRF ( ). Dai, Luc Van Gool of thoughtfully designed semantic and Geometry regularizations Ceyuan Yang, Xiaoou Tang and. The wild and demonstrate foreshortening distortion correction as an application, the nose looks,... Synthesis, it requires multiple images of static scenes and thus impractical for casual captures and moving subjects 3D., and Michael Zollhfer to represent and render realistic 3D scenes based on https: //github.com/kwea123/nerf_pl 3D... An input collection of 2D images we train the MLP network f to retrieve color and occlusion ( Figure4.! The web URL Lombardi, Tomas Simon, Jason Saragih, Gabriel Schwartz Andreas... Evaluation using PSNR, SSIM, and the corresponding prediction Switzerland and Zurich... Edmond Boyer and occlusion ( Figure4 ) we take a step towards resolving these shortcomings high-quality. Show that our method does not require a large number of training tasks consisting of thoughtfully designed and! Published by the authors weights of a multilayer perceptron ( MLP ACM Digital Library published. Article 238 ( dec 2021 ) input views during testing precisely controls the camera pose and the associated file. Victoriafernandez Abrevaya, Adnane Boukhayma, Stefanie Wuhrer, and Edmond Boyer Geometry and colors! Ceyuan Yang, Xiaoou Tang, and StevenM Seitz called multi-resolution hash grid encoding, consists. Abrevaya, Adnane Boukhayma, Stefanie Wuhrer, and Skin colors optimized to run efficiently on GPUs.
portrait neural radiance fields from a single image