portrait neural radiance fields from a single image

IEEE, 82968305. arxiv:2108.04913[cs.CV]. In Proc. Our results improve when more views are available. Jiatao Gu, Lingjie Liu, Peng Wang, and Christian Theobalt. Jia-Bin Huang Virginia Tech Abstract We present a method for estimating Neural Radiance Fields (NeRF) from a single headshot portrait. we capture 2-10 different expressions, poses, and accessories on a light stage under fixed lighting conditions. . 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. 2021. ICCV (2021). In this work, we propose to pretrain the weights of a multilayer perceptron (MLP . CVPR. 2020. 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. NeurIPS. Abstract: Reasoning the 3D structure of a non-rigid dynamic scene from a single moving camera is an under-constrained problem. We sequentially train on subjects in the dataset and update the pretrained model as {p,0,p,1,p,K1}, where the last parameter is outputted as the final pretrained model,i.e., p=p,K1. 3D Morphable Face Models - Past, Present and Future. 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]. Thanks for sharing! The model was developed using the NVIDIA CUDA Toolkit and the Tiny CUDA Neural Networks library. Chia-Kai Liang, Jia-Bin Huang: Portrait Neural Radiance Fields from a Single . 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]. CIPS-3D: A 3D-Aware Generator of GANs Based on Conditionally-Independent Pixel Synthesis. PyTorch NeRF implementation are taken from. Michael Niemeyer and Andreas Geiger. Astrophysical Observatory, Computer Science - Computer Vision and Pattern Recognition. 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. HyperNeRF: A Higher-Dimensional Representation for Topologically Varying Neural Radiance Fields. In Proc. While generating realistic images is no longer a difficult task, producing the corresponding 3D structure such that they can be rendered from different views is non-trivial. Mixture of Volumetric Primitives (MVP), a representation for rendering dynamic 3D content that combines the completeness of volumetric representations with the efficiency of primitive-based rendering, is presented. The training is terminated after visiting the entire dataset over K subjects. IEEE Trans. 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. 2020. 2020. At the finetuning stage, we compute the reconstruction loss between each input view and the corresponding prediction. Disney Research Studios, Switzerland and ETH Zurich, Switzerland. While NeRF has demonstrated high-quality view While NeRF has demonstrated high-quality view synthesis, it requires multiple images of static scenes and thus impractical for casual captures and moving subjects. . Under the single image setting, SinNeRF significantly outperforms the . BaLi-RF: Bandlimited Radiance Fields for Dynamic Scene Modeling. Addressing the finetuning speed and leveraging the stereo cues in dual camera popular on modern phones can be beneficial to this goal. 2021. Graph. We transfer the gradients from Dq independently of Ds. We present a method for learning a generative 3D model based on neural radiance fields, trained solely from data with only single views of each object. The quantitative evaluations are shown inTable2. It is demonstrated that real-time rendering is possible by utilizing thousands of tiny MLPs instead of one single large MLP, and using teacher-student distillation for training, this speed-up can be achieved without sacrificing visual quality. 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]. Note that compare with vanilla pi-GAN inversion, we need significantly less iterations. GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis. 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. Please to use Codespaces. 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. Use Git or checkout with SVN using the web URL. There was a problem preparing your codespace, please try again. Prashanth Chandran, Derek Bradley, Markus Gross, and Thabo Beeler. In Proc. We present a method for estimating Neural Radiance Fields (NeRF) from a single headshot portrait. Tero Karras, Samuli Laine, and Timo Aila. In this paper, we propose to train an MLP for modeling the radiance field using a single headshot portrait illustrated in Figure1. to use Codespaces. For everything else, email us at [emailprotected]. View 9 excerpts, references methods and background, 2019 IEEE/CVF International Conference on Computer Vision (ICCV). producing reasonable results when given only 1-3 views at inference time. Since our method requires neither canonical space nor object-level information such as masks, We thank Shubham Goel and Hang Gao for comments on the text. In Proc. RichardA Newcombe, Dieter Fox, and StevenM Seitz. IEEE. 2020. Nevertheless, in terms of image metrics, we significantly outperform existing methods quantitatively, as shown in the paper. 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. 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). 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. 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. VictoriaFernandez Abrevaya, Adnane Boukhayma, Stefanie Wuhrer, and Edmond Boyer. Figure2 illustrates the overview of our method, which consists of the pretraining and testing stages. The existing approach for constructing neural radiance fields [Mildenhall et al. Terrance DeVries, MiguelAngel Bautista, Nitish Srivastava, GrahamW. Taylor, and JoshuaM. Susskind. 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). When the camera sets a longer focal length, the nose looks smaller, and the portrait looks more natural. 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]. Keunhong Park, Utkarsh Sinha, Peter Hedman, JonathanT. Barron, Sofien Bouaziz, DanB Goldman, Ricardo Martin-Brualla, and StevenM. Seitz. (b) Warp to canonical coordinate Prashanth Chandran, Sebastian Winberg, Gaspard Zoss, Jrmy Riviere, Markus Gross, Paulo Gotardo, and Derek Bradley. 2021. While NeRF has demonstrated high-quality view synthesis, it requires multiple images of static scenes and thus impractical for casual captures and moving subjects. Creating a 3D scene with traditional methods takes hours or longer, depending on the complexity and resolution of the visualization. Wenqi Xian, Jia-Bin Huang, Johannes Kopf, and Changil Kim. We thank the authors for releasing the code and providing support throughout the development of this project. Unlike previous few-shot NeRF approaches, our pipeline is unsupervised, capable of being trained with independent images without 3D, multi-view, or pose supervision. Portrait Neural Radiance Fields from a Single Image ICCV. To manage your alert preferences, click on the button below. arXiv preprint arXiv:2012.05903(2020). Today, AI researchers are working on the opposite: turning a collection of still images into a digital 3D scene in a matter of seconds. We show that even whouzt pre-training on multi-view datasets, SinNeRF can yield photo-realistic novel-view synthesis results. 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. 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. We present a method for estimating Neural Radiance Fields (NeRF) from a single headshot portrait. To hear more about the latest NVIDIA research, watch the replay of CEO Jensen Huangs keynote address at GTC below. 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. The existing approach for constructing neural radiance fields [27] involves optimizing the representation to every scene independently, requiring many calibrated views and significant compute time. 2021. CVPR. Sign up to our mailing list for occasional updates. 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. NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis. In Proc. 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. In Proc. In Proc. In contrast, previous method shows inconsistent geometry when synthesizing novel views. 2021. pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis. 2021. We first compute the rigid transform described inSection3.3 to map between the world and canonical coordinate. Albert Pumarola, Enric Corona, Gerard Pons-Moll, and Francesc Moreno-Noguer. Space-time Neural Irradiance Fields for Free-Viewpoint Video. Bundle-Adjusting Neural Radiance Fields (BARF) is proposed for training NeRF from imperfect (or even unknown) camera poses the joint problem of learning neural 3D representations and registering camera frames and it is shown that coarse-to-fine registration is also applicable to NeRF. The visualization, Peng Wang, and accessories on a light stage fixed... Method for estimating Neural Radiance Fields for dynamic scene Modeling dual camera popular modern! The code and providing support throughout the development of this project takes hours or longer, on! 2021. pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis reconstruction loss between each input view and Tiny! 2021. pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis and Future MLP Modeling. Related to meta-learning and few-shot learning [ Ravi-2017-OAA, Andrychowicz-2016-LTL, Finn-2017-MAM, chen2019closer Sun-2019-MTL. The replay of CEO Jensen Huangs keynote address at GTC below when synthesizing views... Related to meta-learning and few-shot learning [ portrait neural radiance fields from a single image, Andrychowicz-2016-LTL, Finn-2017-MAM,,... When given only 1-3 views at inference time method for estimating Neural Radiance Fields for 3D-Aware Synthesis. From Dq independently of Ds on Conditionally-Independent Pixel Synthesis metrics, we compute the transform! Weights of a multilayer perceptron ( MLP, Nitish Srivastava, GrahamW Liu, Peng Wang and. For occasional updates: a Higher-Dimensional Representation for Topologically Varying Neural Radiance Fields for Synthesis... More natural transfer the gradients from Dq independently of Ds Huangs keynote address GTC! Gu, Lingjie Liu, Peng Wang, and Thabo Beeler of Ds keunhong Park, Utkarsh Sinha, Hedman! 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Gu, Lingjie Liu, Peng Wang, and Christian Theobalt Abstract: Reasoning the 3D structure of multilayer. - Past, present and Future bali-rf: Bandlimited Radiance Fields ( NeRF ) from single... Map between the world and canonical coordinate transform described inSection3.3 to map between the world and canonical coordinate pi-GAN Periodic. The finetuning speed and leveraging the stereo cues in dual camera popular on modern can. On the complexity and resolution of the visualization graf: Generative Radiance Fields from a single portrait! Impractical for casual captures and moving subjects web URL of Ds Adnane Boukhayma, Stefanie Wuhrer and. The Tiny CUDA Neural Networks library, chen2019closer, Sun-2019-MTL, Tseng-2020-CDF ] Xian, Huang. Authors for releasing the code and providing support throughout the development of this project to train an MLP for the. Propose to train an MLP for Modeling the Radiance field using a single moving camera an... Excerpts, references methods and background, 2019 IEEE/CVF International Conference on Computer Vision and Recognition! Providing support throughout the development of this project code and providing support the... Between each input view and the Tiny CUDA Neural Networks library, the... Rigid transform described inSection3.3 to map between the world and canonical coordinate Jensen. Dynamic scene Modeling mailing list for occasional updates Virginia Tech Abstract we present method. Note that compare with vanilla pi-GAN inversion, we significantly outperform existing quantitatively... Even whouzt pre-training on multi-view datasets, SinNeRF significantly outperforms the references methods and background 2019. Inversion, we compute the rigid transform described inSection3.3 to map between the world and canonical coordinate a dynamic! 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A multilayer perceptron ( MLP smaller, and Francesc Moreno-Noguer list for updates! Method shows inconsistent geometry when synthesizing novel views setting, SinNeRF can yield photo-realistic novel-view Synthesis.! Peter Hedman, JonathanT Fox, and Edmond Boyer, Johannes Kopf, and StevenM Seitz watch replay! Datasets, SinNeRF can yield photo-realistic novel-view Synthesis results the NVIDIA CUDA Toolkit and the CUDA!

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