Computer Vision ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 2327, 2022, Proceedings, Part XXII. Portrait Neural Radiance Fields from a Single Image. Dynamic Neural Radiance Fields for Monocular 4D Facial Avatar Reconstruction. 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. 2019. Given a camera pose, one can synthesize the corresponding view by aggregating the radiance over the light ray cast from the camera pose using standard volume rendering. Tero Karras, Miika Aittala, Samuli Laine, Erik Hrknen, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. 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. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. SIGGRAPH '22: ACM SIGGRAPH 2022 Conference Proceedings. While NeRF has demonstrated high-quality view synthesis,. Shugao Ma, Tomas Simon, Jason Saragih, Dawei Wang, Yuecheng Li, Fernando DeLa Torre, and Yaser Sheikh. CVPR. We set the camera viewing directions to look straight to the subject. This model need a portrait video and an image with only background as an inputs. In Proc. To improve the generalization to unseen faces, we train the MLP in the canonical coordinate space approximated by 3D face morphable models. (b) When the input is not a frontal view, the result shows artifacts on the hairs. The ACM Digital Library is published by the Association for Computing Machinery. Copy srn_chairs_train.csv, srn_chairs_train_filted.csv, srn_chairs_val.csv, srn_chairs_val_filted.csv, srn_chairs_test.csv and srn_chairs_test_filted.csv under /PATH_TO/srn_chairs. Then, we finetune the pretrained model parameter p by repeating the iteration in(1) for the input subject and outputs the optimized model parameter s. Leveraging the volume rendering approach of NeRF, our model can be trained directly from images with no explicit 3D supervision. In the pretraining stage, we train a coordinate-based MLP (same in NeRF) f on diverse subjects captured from the light stage and obtain the pretrained model parameter optimized for generalization, denoted as p(Section3.2). Our experiments show favorable quantitative results against the state-of-the-art 3D face reconstruction and synthesis algorithms on the dataset of controlled captures. (b) Warp to canonical coordinate We hold out six captures for testing. 2020. (c) Finetune. 2021. Our method finetunes the pretrained model on (a), and synthesizes the new views using the controlled camera poses (c-g) relative to (a). ACM Trans. There was a problem preparing your codespace, please try again. Are you sure you want to create this branch? We present a method for estimating Neural Radiance Fields (NeRF) from a single headshot portrait. We use pytorch 1.7.0 with CUDA 10.1. Please download the datasets from these links: Please download the depth from here: https://drive.google.com/drive/folders/13Lc79Ox0k9Ih2o0Y9e_g_ky41Nx40eJw?usp=sharing. 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]. We present a method for estimating Neural Radiance Fields (NeRF) from a single headshot portrait. 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. CVPR. 2018. arXiv preprint arXiv:2106.05744(2021). Instances should be directly within these three folders. We show that, unlike existing methods, one does not need multi-view . 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 The University of Texas at Austin, Austin, USA. Abstract. Ablation study on different weight initialization. Christopher Xie, Keunhong Park, Ricardo Martin-Brualla, and Matthew Brown. 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. 2021. i3DMM: Deep Implicit 3D Morphable Model of Human Heads. We are interested in generalizing our method to class-specific view synthesis, such as cars or human bodies. Experimental results demonstrate that the novel framework can produce high-fidelity and natural results, and support free adjustment of audio signals, viewing directions, and background images. CoRR abs/2012.05903 (2020), Copyright 2023 Sanghani Center for Artificial Intelligence and Data Analytics, Sanghani Center for Artificial Intelligence and Data Analytics. ACM Trans. VictoriaFernandez Abrevaya, Adnane Boukhayma, Stefanie Wuhrer, and Edmond Boyer. This paper introduces a method to modify the apparent relative pose and distance between camera and subject given a single portrait photo, and builds a 2D warp in the image plane to approximate the effect of a desired change in 3D. At the finetuning stage, we compute the reconstruction loss between each input view and the corresponding prediction. NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections. [1/4]" 2021. pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image 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 show that even whouzt pre-training on multi-view datasets, SinNeRF can yield photo-realistic novel-view synthesis results. 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. While NeRF has demonstrated high-quality view synthesis, it requires multiple images of static scenes and thus impractical for casual captures and moving subjects. Ziyan Wang, Timur Bagautdinov, Stephen Lombardi, Tomas Simon, Jason Saragih, Jessica Hodgins, and Michael Zollhfer. To improve the generalization to unseen faces, we train the MLP in the canonical coordinate space approximated by 3D face morphable models. FiG-NeRF: Figure-Ground Neural Radiance Fields for 3D Object Category Modelling. 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. We introduce the novel CFW module to perform expression conditioned warping in 2D feature space, which is also identity adaptive and 3D constrained. StyleNeRF: A Style-based 3D Aware Generator for High-resolution Image Synthesis. Image2StyleGAN++: How to edit the embedded 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. \underbracket\pagecolorwhiteInput \underbracket\pagecolorwhiteOurmethod \underbracket\pagecolorwhiteGroundtruth. Learn more. Our dataset consists of 70 different individuals with diverse gender, races, ages, skin colors, hairstyles, accessories, and costumes. 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 a scene that includes people or other moving elements, the quicker these shots are captured, the better. The neural network for parametric mapping is elaborately designed to maximize the solution space to represent diverse identities and expressions. 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. to use Codespaces. Second, we propose to train the MLP in a canonical coordinate by exploiting domain-specific knowledge about the face shape. 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. Our method takes a lot more steps in a single meta-training task for better convergence. 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. Towards a complete 3D morphable model of the human head. Extrapolating the camera pose to the unseen poses from the training data is challenging and leads to artifacts. arXiv as responsive web pages so you InTable4, we show that the validation performance saturates after visiting 59 training tasks. In this work, we propose to pretrain the weights of a multilayer perceptron (MLP . The code repo is built upon https://github.com/marcoamonteiro/pi-GAN. [Jackson-2017-LP3] only covers the face area. At the test time, we initialize the NeRF with the pretrained model parameter p and then finetune it on the frontal view for the input subject s. 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. In Proc. 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. In Proc. 2020. [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. We propose pixelNeRF, a learning framework that predicts a continuous neural scene representation conditioned on one or few input images. H3D-Net: Few-Shot High-Fidelity 3D Head Reconstruction. Our method precisely controls the camera pose, and faithfully reconstructs the details from the subject, as shown in the insets. CVPR. 2020. During the prediction, we first warp the input coordinate from the world coordinate to the face canonical space through (sm,Rm,tm). 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. 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. Anurag Ranjan, Timo Bolkart, Soubhik Sanyal, and MichaelJ. 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 quantitative evaluations are shown inTable2. Comparisons. We proceed the update using the loss between the prediction from the known camera pose and the query dataset Dq. The subjects cover various ages, gender, races, and skin colors. 3D Morphable Face Models - Past, Present and Future. No description, website, or topics provided. 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]. In addition, we show thenovel application of a perceptual loss on the image space is critical forachieving photorealism. ICCV (2021). Yujun Shen, Ceyuan Yang, Xiaoou Tang, and Bolei Zhou. Please send any questions or comments to Alex Yu. 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. 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). 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 . This note is an annotated bibliography of the relevant papers, and the associated bibtex file on the repository. producing reasonable results when given only 1-3 views at inference time. Google Scholar Google Inc. Abstract and Figures We present a method for estimating Neural Radiance Fields (NeRF) from a single headshot portrait. CVPR. Since our model is feed-forward and uses a relatively compact latent codes, it most likely will not perform that well on yourself/very familiar faces---the details are very challenging to be fully captured by a single pass. Today, AI researchers are working on the opposite: turning a collection of still images into a digital 3D scene in a matter of seconds. Our method preserves temporal coherence in challenging areas like hairs and occlusion, such as the nose and ears. p,mUpdates by (1)mUpdates by (2)Updates by (3)p,m+1. Initialization. Training NeRFs for different subjects is analogous to training classifiers for various tasks. 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. Check if you have access through your login credentials or your institution to get full access on this article. NeuIPS, H.Larochelle, M.Ranzato, R.Hadsell, M.F. Balcan, and H.Lin (Eds.). 2020. "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). 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. 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. Notice, Smithsonian Terms of 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. More finetuning with smaller strides benefits reconstruction quality. Stylianos Ploumpis, Evangelos Ververas, Eimear OSullivan, Stylianos Moschoglou, Haoyang Wang, Nick Pears, William Smith, Baris Gecer, and StefanosP Zafeiriou. NeurIPS. IEEE, 81108119. Thanks for sharing! Unlike previous few-shot NeRF approaches, our pipeline is unsupervised, capable of being trained with independent images without 3D, multi-view, or pose supervision. Astrophysical Observatory, Computer Science - Computer Vision and Pattern Recognition. In this work, we make the following contributions: We present a single-image view synthesis algorithm for portrait photos by leveraging meta-learning. 1. Daniel Vlasic, Matthew Brand, Hanspeter Pfister, and Jovan Popovi. Analyzing and improving the image quality of StyleGAN. 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. Feed-forward NeRF from One View. Input views in test time. NeRFs use neural networks to represent and render realistic 3D scenes based on an input collection of 2D images. Zixun Yu: from Purdue, on portrait image enhancement (2019) Wei-Shang Lai: from UC Merced, on wide-angle portrait distortion correction (2018) Publications. 2021. 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. While the quality of these 3D model-based methods has been improved dramatically via deep networks[Genova-2018-UTF, Xu-2020-D3P], a common limitation is that the model only covers the center of the face and excludes the upper head, hairs, and torso, due to their high variability. However, training the MLP requires capturing images of static subjects from multiple viewpoints (in the order of 10-100 images)[Mildenhall-2020-NRS, Martin-2020-NIT]. 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. IEEE, 82968305. ICCV. 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. Tianye Li, Timo Bolkart, MichaelJ. We use cookies to ensure that we give you the best experience on our website. Generating 3D faces using Convolutional Mesh Autoencoders. Rendering with Style: Combining Traditional and Neural Approaches for High-Quality Face Rendering. IEEE, 44324441. 39, 5 (2020). to use Codespaces. 8649-8658. Our FDNeRF supports free edits of facial expressions, and enables video-driven 3D reenactment. 2021b. We quantitatively evaluate the method using controlled captures and demonstrate the generalization to real portrait images, showing favorable results against state-of-the-arts. , denoted as LDs(fm). such as pose manipulation[Criminisi-2003-GMF], This is a challenging task, as training NeRF requires multiple views of the same scene, coupled with corresponding poses, which are hard to obtain. Codebase based on https://github.com/kwea123/nerf_pl . Therefore, we provide a script performing hybrid optimization: predict a latent code using our model, then perform latent optimization as introduced in pi-GAN. We refer to the process training a NeRF model parameter for subject m from the support set as a task, denoted by Tm. NeurIPS. Render images and a video interpolating between 2 images. Specifically, SinNeRF constructs a semi-supervised learning process, where we introduce and propagate geometry pseudo labels and semantic pseudo labels to guide the progressive training process. While NeRF has demonstrated high-quality view synthesis, it requires multiple images of static scenes and thus impractical for casual captures and moving subjects. 2022. [width=1]fig/method/pretrain_v5.pdf 2020. Michael Niemeyer and Andreas Geiger. The center view corresponds to the front view expected at the test time, referred to as the support set Ds, and the remaining views are the target for view synthesis, referred to as the query set Dq. Dynamic Neural Radiance Fields for Monocular 4D Facial Avatar Reconstruction. 2015. Our method takes the benefits from both face-specific modeling and view synthesis on generic scenes. Recent research work has developed powerful generative models (e.g., StyleGAN2) that can synthesize complete human head images with impressive photorealism, enabling applications such as photorealistically editing real photographs. 40, 6 (dec 2021). The warp makes our method robust to the variation in face geometry and pose in the training and testing inputs, as shown inTable3 andFigure10. Are you sure you want to create this branch? Our work is a first step toward the goal that makes NeRF practical with casual captures on hand-held devices. without modification. In all cases, pixelNeRF outperforms current state-of-the-art baselines for novel view synthesis and single image 3D reconstruction. We further demonstrate the flexibility of pixelNeRF by demonstrating it on multi-object ShapeNet scenes and real scenes from the DTU dataset. We show that our method can also conduct wide-baseline view synthesis on more complex real scenes from the DTU MVS dataset, In our method, the 3D model is used to obtain the rigid transform (sm,Rm,tm). Figure9 compares the results finetuned from different initialization methods. Portrait Neural Radiance Fields from a Single Image Alias-Free Generative Adversarial Networks. We also address the shape variations among subjects by learning the NeRF model in canonical face space. Pixel Codec Avatars. arXiv Vanity renders academic papers from Graph. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. SpiralNet++: A Fast and Highly Efficient Mesh Convolution Operator. Albert Pumarola, Enric Corona, Gerard Pons-Moll, and Francesc Moreno-Noguer. To improve the, 2021 IEEE/CVF International Conference on Computer Vision (ICCV). We manipulate the perspective effects such as dolly zoom in the supplementary materials. PAMI 23, 6 (jun 2001), 681685. When the camera sets a longer focal length, the nose looks smaller, and the portrait looks more natural. 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.