portrait neural radiance fields from a single image

CVPR. In Proc. In a scene that includes people or other moving elements, the quicker these shots are captured, the better. 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. Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. [Xu-2020-D3P] generates plausible results but fails to preserve the gaze direction, facial expressions, face shape, and the hairstyles (the bottom row) when comparing to the ground truth. 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]. Abstract. 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. The subjects cover various ages, gender, races, and skin colors. Our method requires the input subject to be roughly in frontal view and does not work well with the profile view, as shown inFigure12(b). 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. While NeRF has demonstrated high-quality view Under the single image setting, SinNeRF significantly outperforms the . To attain this goal, we present a Single View NeRF (SinNeRF) framework consisting of thoughtfully designed semantic and geometry regularizations. Notice, Smithsonian Terms of In Proc. NeRF[Mildenhall-2020-NRS] represents the scene as a mapping F from the world coordinate and viewing direction to the color and occupancy using a compact MLP. 2020. There was a problem preparing your codespace, please try again. The disentangled parameters of shape, appearance and expression can be interpolated to achieve a continuous and morphable facial synthesis. We report the quantitative evaluation using PSNR, SSIM, and LPIPS[zhang2018unreasonable] against the ground truth inTable1. 2021. Use, Smithsonian TL;DR: Given only a single reference view as input, our novel semi-supervised framework trains a neural radiance field effectively. 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]. 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. Glean Founders Talk AI-Powered Enterprise Search, Generative AI at GTC: Dozens of Sessions to Feature Luminaries Speaking on Techs Hottest Topic, Fusion Reaction: How AI, HPC Are Energizing Science, Flawless Fractal Food Featured This Week In the NVIDIA Studio. When the first instant photo was taken 75 years ago with a Polaroid camera, it was groundbreaking to rapidly capture the 3D world in a realistic 2D image. Recent research indicates that we can make this a lot faster by eliminating deep learning. to use Codespaces. (c) Finetune. 2021a. In International Conference on 3D Vision. 2020] Prashanth Chandran, Derek Bradley, Markus Gross, and Thabo Beeler. In Proc. CVPR. 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. Reconstructing face geometry and texture enables view synthesis using graphics rendering pipelines. 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. arXiv preprint arXiv:2012.05903(2020). 2019. p,mUpdates by (1)mUpdates by (2)Updates by (3)p,m+1. Our approach operates in view-spaceas opposed to canonicaland requires no test-time optimization. 2020. Zhengqi Li, Simon Niklaus, Noah Snavely, and Oliver Wang. Analyzing and improving the image quality of StyleGAN. NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections. in ShapeNet in order to perform novel-view synthesis on unseen objects. To hear more about the latest NVIDIA research, watch the replay of CEO Jensen Huangs keynote address at GTC below. Project page: https://vita-group.github.io/SinNeRF/ Nerfies: Deformable Neural Radiance Fields. Input views in test time. While NeRF has demonstrated high-quality view synthesis, it requires multiple images of static scenes and thus impractical for casual captures and moving subjects. 343352. To demonstrate generalization capabilities, Each subject is lit uniformly under controlled lighting conditions. We refer to the process training a NeRF model parameter for subject m from the support set as a task, denoted by Tm. Check if you have access through your login credentials or your institution to get full access on this article. The existing approach for constructing neural radiance fields [Mildenhall et al. Our method preserves temporal coherence in challenging areas like hairs and occlusion, such as the nose and ears. Perspective manipulation. Zixun Yu: from Purdue, on portrait image enhancement (2019) Wei-Shang Lai: from UC Merced, on wide-angle portrait distortion correction (2018) Publications. Figure2 illustrates the overview of our method, which consists of the pretraining and testing stages. Yujun Shen, Ceyuan Yang, Xiaoou Tang, and Bolei Zhou. Rigid transform between the world and canonical face coordinate. 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. 2020. 2021. At the finetuning stage, we compute the reconstruction loss between each input view and the corresponding prediction. In this work, we consider a more ambitious task: training neural radiance field, over realistically complex visual scenes, by looking only once, i.e., using only a single view. More finetuning with smaller strides benefits reconstruction quality. In that sense, Instant NeRF could be as important to 3D as digital cameras and JPEG compression have been to 2D photography vastly increasing the speed, ease and reach of 3D capture and sharing.. 3D Morphable Face Models - Past, Present and Future. Showcased in a session at NVIDIA GTC this week, Instant NeRF could be used to create avatars or scenes for virtual worlds, to capture video conference participants and their environments in 3D, or to reconstruct scenes for 3D digital maps. View 4 excerpts, cites background and methods. Our method takes the benefits from both face-specific modeling and view synthesis on generic scenes. ICCV. Portrait view synthesis enables various post-capture edits and computer vision applications, Eduard Ramon, Gil Triginer, Janna Escur, Albert Pumarola, Jaime Garcia, Xavier Giro-i Nieto, and Francesc Moreno-Noguer. While simply satisfying the radiance field over the input image does not guarantee a correct geometry, . 2015. "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). one or few input images. View 9 excerpts, references methods and background, 2019 IEEE/CVF International Conference on Computer Vision (ICCV). Stephen Lombardi, Tomas Simon, Jason Saragih, Gabriel Schwartz, Andreas Lehrmann, and Yaser Sheikh. Please let the authors know if results are not at reasonable levels! SinNeRF: Training Neural Radiance Fields on Complex Scenes from a Single Image [Paper] [Website] Pipeline Code Environment pip install -r requirements.txt Dataset Preparation Please download the datasets from these links: NeRF synthetic: Download nerf_synthetic.zip from https://drive.google.com/drive/folders/128yBriW1IG_3NJ5Rp7APSTZsJqdJdfc1 ICCV. Our experiments show favorable quantitative results against the state-of-the-art 3D face reconstruction and synthesis algorithms on the dataset of controlled captures. Computer Vision ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 2327, 2022, Proceedings, Part XXII. constructing neural radiance fields[Mildenhall et al. Using 3D morphable model, they apply facial expression tracking. 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. You signed in with another tab or window. We present a method for estimating Neural Radiance Fields (NeRF) from a single headshot portrait. 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. 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. 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. 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. 2021. To address the face shape variations in the training dataset and real-world inputs, we normalize the world coordinate to the canonical space using a rigid transform and apply f on the warped coordinate. 2021. In this work, we propose to pretrain the weights of a multilayer perceptron (MLP), which implicitly models the volumetric density and . 99. Portrait Neural Radiance Fields from a Single Image. Generating 3D faces using Convolutional Mesh Autoencoders. such as pose manipulation[Criminisi-2003-GMF], Work fast with our official CLI. 40, 6 (dec 2021). Rendering with Style: Combining Traditional and Neural Approaches for High-Quality Face Rendering. HoloGAN is the first generative model that learns 3D representations from natural images in an entirely unsupervised manner and is shown to be able to generate images with similar or higher visual quality than other generative models. We further demonstrate the flexibility of pixelNeRF by demonstrating it on multi-object ShapeNet scenes and real scenes from the DTU dataset. 2017. Single Image Deblurring with Adaptive Dictionary Learning Zhe Hu, . 2019. 2021. We present a method for estimating Neural Radiance Fields (NeRF) from a single headshot portrait. Figure5 shows our results on the diverse subjects taken in the wild. CVPR. RichardA Newcombe, Dieter Fox, and StevenM Seitz. involves optimizing the representation to every scene independently, requiring many calibrated views and significant compute time. 2021. 2021. i3DMM: Deep Implicit 3D Morphable Model of Human Heads. Anurag Ranjan, Timo Bolkart, Soubhik Sanyal, and MichaelJ. CVPR. 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. SpiralNet++: A Fast and Highly Efficient Mesh Convolution Operator. Image2StyleGAN: How to embed images into the StyleGAN latent space?. 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. ACM Trans. While NeRF has demonstrated high-quality view synthesis, it requires multiple images of static scenes and thus impractical for casual captures and moving subjects. (or is it just me), Smithsonian Privacy 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). In International Conference on Learning Representations. Since our method requires neither canonical space nor object-level information such as masks, Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. Instant NeRF is a neural rendering model that learns a high-resolution 3D scene in seconds and can render images of that scene in a few milliseconds. In total, our dataset consists of 230 captures. In Proc. Figure10 andTable3 compare the view synthesis using the face canonical coordinate (Section3.3) to the world coordinate. Chia-Kai Liang, Jia-Bin Huang: Portrait Neural Radiance Fields from a Single . Curran Associates, Inc., 98419850. For each task Tm, we train the model on Ds and Dq alternatively in an inner loop, as illustrated in Figure3. 2021. pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis. arxiv:2110.09788[cs, eess], All Holdings within the ACM Digital Library. 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 . Training task size. We address the challenges in two novel ways. 2020. Urban Radiance Fieldsallows for accurate 3D reconstruction of urban settings using panoramas and lidar information by compensating for photometric effects and supervising model training with lidar-based depth. To improve the generalization to unseen faces, we train the MLP in the canonical coordinate space approximated by 3D face morphable models. It relies on a technique developed by NVIDIA called multi-resolution hash grid encoding, which is optimized to run efficiently on NVIDIA GPUs. Learning Compositional Radiance Fields of Dynamic Human Heads. Black, Hao Li, and Javier Romero. 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. If nothing happens, download Xcode and try again. 86498658. arXiv as responsive web pages so you NeRFs use neural networks to represent and render realistic 3D scenes based on an input collection of 2D images. We presented a method for portrait view synthesis using a single headshot photo. Title:Portrait Neural Radiance Fields from a Single Image Authors:Chen Gao, Yichang Shih, Wei-Sheng Lai, Chia-Kai Liang, Jia-Bin Huang Download PDF Abstract:We present a method for estimating Neural Radiance Fields (NeRF) from a single headshot portrait. We thank Shubham Goel and Hang Gao for comments on the text. We transfer the gradients from Dq independently of Ds. Codebase based on https://github.com/kwea123/nerf_pl . (a) When the background is not removed, our method cannot distinguish the background from the foreground and leads to severe artifacts. Katja Schwarz, Yiyi Liao, Michael Niemeyer, and Andreas Geiger. 33. A Decoupled 3D Facial Shape Model by Adversarial Training. Figure6 compares our results to the ground truth using the subject in the test hold-out set. Black. In Proc. NeurIPS. H3D-Net: Few-Shot High-Fidelity 3D Head Reconstruction. Star Fork. Instead of training the warping effect between a set of pre-defined focal lengths[Zhao-2019-LPU, Nagano-2019-DFN], our method achieves the perspective effect at arbitrary camera distances and focal lengths. The code repo is built upon https://github.com/marcoamonteiro/pi-GAN. Graph. arXiv Vanity renders academic papers from 2020. GANSpace: Discovering Interpretable GAN Controls. For better generalization, the gradients of Ds will be adapted from the input subject at the test time by finetuning, instead of transferred from the training data. CVPR. Please use --split val for NeRF synthetic dataset. Graphics (Proc. Unconstrained Scene Generation with Locally Conditioned Radiance Fields. We quantitatively evaluate the method using controlled captures and demonstrate the generalization to real portrait images, showing favorable results against state-of-the-arts. Dynamic Neural Radiance Fields for Monocular 4D Facial Avatar Reconstruction. Pivotal Tuning for Latent-based Editing of Real Images. We present a method for estimating Neural Radiance Fields (NeRF) from a single headshot portrait. We propose pixelNeRF, a learning framework that predicts a continuous neural scene representation conditioned on one or few input images. 2020. If nothing happens, download GitHub Desktop and try again. FLAME-in-NeRF : Neural control of Radiance Fields for Free View Face Animation. Our results faithfully preserve the details like skin textures, personal identity, and facial expressions from the input. The results in (c-g) look realistic and natural. We present a method for estimating Neural Radiance Fields (NeRF) from a single headshot portrait. (b) When the input is not a frontal view, the result shows artifacts on the hairs. Portrait Neural Radiance Fields from a Single Image. CVPR. Please In International Conference on 3D Vision (3DV). 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. In all cases, pixelNeRF outperforms current state-of-the-art baselines for novel view synthesis and single image 3D reconstruction. 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]. Albert Pumarola, Enric Corona, Gerard Pons-Moll, and Francesc Moreno-Noguer. 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. 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. Semantic Deep Face Models. 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. We use the finetuned model parameter (denoted by s) for view synthesis (Section3.4). SinNeRF: Training Neural Radiance Fields on Complex Scenes from a Single Image, https://drive.google.com/drive/folders/128yBriW1IG_3NJ5Rp7APSTZsJqdJdfc1, https://drive.google.com/file/d/1eDjh-_bxKKnEuz5h-HXS7EDJn59clx6V/view, https://drive.google.com/drive/folders/13Lc79Ox0k9Ih2o0Y9e_g_ky41Nx40eJw?usp=sharing, DTU: Download the preprocessed DTU training data from. Or, have a go at fixing it yourself the renderer is open source! While NeRF has demonstrated high-quality view synthesis,. The existing approach for D-NeRF: Neural Radiance Fields for Dynamic Scenes. IEEE, 82968305. Are you sure you want to create this branch? NeRF or better known as Neural Radiance Fields is a state . When the camera sets a longer focal length, the nose looks smaller, and the portrait looks more natural. PAMI (2020). We set the camera viewing directions to look straight to the subject. Graph. SIGGRAPH '22: ACM SIGGRAPH 2022 Conference Proceedings. Jrmy Riviere, Paulo Gotardo, Derek Bradley, Abhijeet Ghosh, and Thabo Beeler. [Jackson-2017-LP3] only covers the face area. Despite the rapid development of Neural Radiance Field (NeRF), the necessity of dense covers largely prohibits its wider applications. HyperNeRF: A Higher-Dimensional Representation for Topologically Varying Neural Radiance Fields. CVPR. To manage your alert preferences, click on the button below. Ablation study on different weight initialization. CIPS-3D: A 3D-Aware Generator of GANs Based on Conditionally-Independent Pixel Synthesis. 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. We manipulate the perspective effects such as dolly zoom in the supplementary materials. 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). In Proc. Google Scholar Cross Ref; Chen Gao, Yichang Shih, Wei-Sheng Lai, Chia-Kai Liang, and Jia-Bin Huang. PlenOctrees for Real-time Rendering of Neural Radiance Fields. We propose pixelNeRF, a learning framework that predicts a continuous neural scene representation conditioned on Learn more. Alias-Free Generative Adversarial Networks. 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. In each row, we show the input frontal view and two synthesized views using. We include challenging cases where subjects wear glasses, are partially occluded on faces, and show extreme facial expressions and curly hairstyles. Agreement NNX16AC86A, Is ADS down? Please Are you sure you want to create this branch? CVPR. Proc. [width=1]fig/method/overview_v3.pdf , denoted as LDs(fm). Keunhong Park, Utkarsh Sinha, JonathanT. Barron, Sofien Bouaziz, DanB Goldman, StevenM. Seitz, and Ricardo Martin-Brualla. 2021. Extrapolating the camera pose to the unseen poses from the training data is challenging and leads to artifacts. 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. Under the single image setting, SinNeRF significantly outperforms the current state-of-the-art NeRF baselines in all cases. To manage your alert preferences, click on the button below. ICCV. 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. We show the evaluations on different number of input views against the ground truth inFigure11 and comparisons to different initialization inTable5. 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. Astrophysical Observatory, Computer Science - Computer Vision and Pattern Recognition. The method is based on an autoencoder that factors each input image into depth. Face pose manipulation. We present a method for estimating Neural Radiance Fields (NeRF) from a single headshot portrait. The videos are accompanied in the supplementary materials. During the prediction, we first warp the input coordinate from the world coordinate to the face canonical space through (sm,Rm,tm). In Proc. View 4 excerpts, references background and methods. Graph. 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". 2019. ACM Trans. Face Transfer with Multilinear Models. 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. Similarly to the neural volume method[Lombardi-2019-NVL], our method improves the rendering quality by sampling the warped coordinate from the world coordinates.
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