portrait neural radiance fields from a single image

In Proc. Our method preserves temporal coherence in challenging areas like hairs and occlusion, such as the nose and ears. We thank the authors for releasing the code and providing support throughout the development of this project. Figure9(b) shows that such a pretraining approach can also learn geometry prior from the dataset but shows artifacts in view synthesis. Ben Mildenhall, PratulP. Srinivasan, Matthew Tancik, JonathanT. Barron, Ravi Ramamoorthi, and Ren Ng. While NeRF has demonstrated high-quality view synthesis, it requires multiple images of static scenes and thus impractical for casual captures and moving subjects. 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. 2019. 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]. ICCV. 2020. 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. NeRF fits multi-layer perceptrons (MLPs) representing view-invariant opacity and view-dependent color volumes to a set of training images, and samples novel views based on volume . Please download the datasets from these links: Please download the depth from here: https://drive.google.com/drive/folders/13Lc79Ox0k9Ih2o0Y9e_g_ky41Nx40eJw?usp=sharing. 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. Christopher Xie, Keunhong Park, Ricardo Martin-Brualla, and Matthew Brown. Rendering with Style: Combining Traditional and Neural Approaches for High-Quality Face Rendering. To manage your alert preferences, click on the button below. Input views in test time. We introduce the novel CFW module to perform expression conditioned warping in 2D feature space, which is also identity adaptive and 3D constrained. 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. The neural network for parametric mapping is elaborately designed to maximize the solution space to represent diverse identities and expressions. ICCV. python linear_interpolation --path=/PATH_TO/checkpoint_train.pth --output_dir=/PATH_TO_WRITE_TO/. Volker Blanz and Thomas Vetter. A parametrization issue involved in applying NeRF to 360 captures of objects within large-scale, unbounded 3D scenes is addressed, and the method improves view synthesis fidelity in this challenging scenario. 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). Peng Zhou, Lingxi Xie, Bingbing Ni, and Qi Tian. one or few input images. FiG-NeRF: Figure-Ground Neural Radiance Fields for 3D Object Category Modelling. 2021. 2021. 86498658. Jiatao Gu, Lingjie Liu, Peng Wang, and Christian Theobalt. View 9 excerpts, references methods and background, 2019 IEEE/CVF International Conference on Computer Vision (ICCV). Addressing the finetuning speed and leveraging the stereo cues in dual camera popular on modern phones can be beneficial to this goal. CVPR. If nothing happens, download Xcode and try again. Pix2NeRF: Unsupervised Conditional -GAN for Single Image to Neural Radiance Fields Translation Our method using (c) canonical face coordinate shows better quality than using (b) world coordinate on chin and eyes. 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. In International Conference on 3D Vision (3DV). https://dl.acm.org/doi/10.1145/3528233.3530753. Since Dq is unseen during the test time, we feedback the gradients to the pretrained parameter p,m to improve generalization. 3D face modeling. 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. Future work. producing reasonable results when given only 1-3 views at inference time. For example, Neural Radiance Fields (NeRF) demonstrates high-quality view synthesis by implicitly modeling the volumetric density and color using the weights of a multilayer perceptron (MLP). 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. Our results improve when more views are available. (c) Finetune. We average all the facial geometries in the dataset to obtain the mean geometry F. The MLP is trained by minimizing the reconstruction loss between synthesized views and the corresponding ground truth input images. Training task size. It could also be used in architecture and entertainment to rapidly generate digital representations of real environments that creators can modify and build on. (a) When the background is not removed, our method cannot distinguish the background from the foreground and leads to severe artifacts. by introducing an architecture that conditions a NeRF on image inputs in a fully convolutional manner. 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. Shugao Ma, Tomas Simon, Jason Saragih, Dawei Wang, Yuecheng Li, Fernando DeLa Torre, and Yaser Sheikh. There was a problem preparing your codespace, please try again. Our method does not require a large number of training tasks consisting of many subjects. We use pytorch 1.7.0 with CUDA 10.1. 2021. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. to use Codespaces. Our method focuses on headshot portraits and uses an implicit function as the neural representation. NeRFs use neural networks to represent and render realistic 3D scenes based on an input collection of 2D images. 1999. Learn more. . In Proc. The learning-based head reconstruction method from Xuet al. 2021. 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. Want to hear about new tools we're making? By virtually moving the camera closer or further from the subject and adjusting the focal length correspondingly to preserve the face area, we demonstrate perspective effect manipulation using portrait NeRF inFigure8 and the supplemental video. The results from [Xu-2020-D3P] were kindly provided by the authors. We use cookies to ensure that we give you the best experience on our website. Figure10 andTable3 compare the view synthesis using the face canonical coordinate (Section3.3) to the world coordinate. Proc. 2020. 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 results faithfully preserve the details like skin textures, personal identity, and facial expressions from the input. Applications of our pipeline include 3d avatar generation, object-centric novel view synthesis with a single input image, and 3d-aware super-resolution, to name a few. , denoted as LDs(fm). 33. Our method takes a lot more steps in a single meta-training task for better convergence. In Proc. Beyond NeRFs, NVIDIA researchers are exploring how this input encoding technique might be used to accelerate multiple AI challenges including reinforcement learning, language translation and general-purpose deep learning algorithms. ICCV. Astrophysical Observatory, Computer Science - Computer Vision and Pattern Recognition. Star Fork. Rameen Abdal, Yipeng Qin, and Peter Wonka. 343352. Chen Gao, Yichang Shih, Wei-Sheng Lai, Chia-Kai Liang, and Jia-Bin Huang. 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]. Stephen Lombardi, Tomas Simon, Jason Saragih, Gabriel Schwartz, Andreas Lehrmann, and Yaser Sheikh. 2021. pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis. We hold out six captures for testing. 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. 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. 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. Portraits taken by wide-angle cameras exhibit undesired foreshortening distortion due to the perspective projection [Fried-2016-PAM, Zhao-2019-LPU]. Codebase based on https://github.com/kwea123/nerf_pl . InTable4, we show that the validation performance saturates after visiting 59 training tasks. 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. We thank Shubham Goel and Hang Gao for comments on the text. To manage your alert preferences, click on the button below. TimothyF. Cootes, GarethJ. Edwards, and ChristopherJ. Taylor. 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. Abstract. In Proc. Training NeRFs for different subjects is analogous to training classifiers for various tasks. 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. 2020. If nothing happens, download GitHub Desktop and try again. Our experiments show favorable quantitative results against the state-of-the-art 3D face reconstruction and synthesis algorithms on the dataset of controlled captures. We first compute the rigid transform described inSection3.3 to map between the world and canonical coordinate. IEEE Trans. 345354. The disentangled parameters of shape, appearance and expression can be interpolated to achieve a continuous and morphable facial synthesis. NeurIPS. The command to use is: python --path PRETRAINED_MODEL_PATH --output_dir OUTPUT_DIRECTORY --curriculum ["celeba" or "carla" or "srnchairs"] --img_path /PATH_TO_IMAGE_TO_OPTIMIZE/ Graph. While NeRF has demonstrated high-quality view synthesis, it requires multiple images of static scenes and thus impractical for casual captures and moving subjects. IEEE, 82968305. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Note that the training script has been refactored and has not been fully validated yet. This model need a portrait video and an image with only background as an inputs. 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. In Proc. 2021. Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenes. 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. 99. We obtain the results of Jacksonet al. Initialization. Graph. 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]. 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. Portrait view synthesis enables various post-capture edits and computer vision applications, 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. 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. The process, however, requires an expensive hardware setup and is unsuitable for casual users. In a scene that includes people or other moving elements, the quicker these shots are captured, the better. While NeRF has demonstrated high-quality view synthesis, it requires multiple images of static scenes and thus impractical for casual captures and moving subjects. 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. 8649-8658. To balance the training size and visual quality, we use 27 subjects for the results shown in this paper. Today, AI researchers are working on the opposite: turning a collection of still images into a digital 3D scene in a matter of seconds. Stylianos Ploumpis, Evangelos Ververas, Eimear OSullivan, Stylianos Moschoglou, Haoyang Wang, Nick Pears, William Smith, Baris Gecer, and StefanosP Zafeiriou. \underbracket\pagecolorwhiteInput \underbracket\pagecolorwhiteOurmethod \underbracket\pagecolorwhiteGroundtruth. This work introduces three objectives: a batch distribution loss that encourages the output distribution to match the distribution of the morphable model, a loopback loss that ensures the network can correctly reinterpret its own output, and a multi-view identity loss that compares the features of the predicted 3D face and the input photograph from multiple viewing angles. We propose pixelNeRF, a learning framework that predicts a continuous neural scene representation conditioned on one or few input images. Our method can incorporate multi-view inputs associated with known camera poses to improve the view synthesis quality. Tarun Yenamandra, Ayush Tewari, Florian Bernard, Hans-Peter Seidel, Mohamed Elgharib, Daniel Cremers, and Christian Theobalt. CVPR. Daniel Roich, Ron Mokady, AmitH Bermano, and Daniel Cohen-Or. A style-based generator architecture for generative adversarial networks. Ablation study on the number of input views during testing. 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. Ziyan Wang, Timur Bagautdinov, Stephen Lombardi, Tomas Simon, Jason Saragih, Jessica Hodgins, and Michael Zollhfer. such as pose manipulation[Criminisi-2003-GMF], Using multiview image supervision, we train a single pixelNeRF to 13 largest object . 2021. We quantitatively evaluate the method using controlled captures and demonstrate the generalization to real portrait images, showing favorable results against state-of-the-arts. For each task Tm, we train the model on Ds and Dq alternatively in an inner loop, as illustrated in Figure3. S. Gong, L. Chen, M. Bronstein, and S. Zafeiriou. The result, dubbed Instant NeRF, is the fastest NeRF technique to date, achieving more than 1,000x speedups in some cases. In contrast, our method requires only one single image as input. ACM Trans. arXiv preprint arXiv:2110.09788(2021). CVPR. [1/4]" 36, 6 (nov 2017), 17pages. By clicking accept or continuing to use the site, you agree to the terms outlined in our. 2020. 2020. arxiv:2108.04913[cs.CV]. (pdf) Articulated A second emerging trend is the application of neural radiance field for articulated models of people, or cats : ACM Trans. H3D-Net: Few-Shot High-Fidelity 3D Head Reconstruction. We conduct extensive experiments on ShapeNet benchmarks for single image novel view synthesis tasks with held-out objects as well as entire unseen categories. Our method builds upon the recent advances of neural implicit representation and addresses the limitation of generalizing to an unseen subject when only one single image is available. Stephen Lombardi, Tomas Simon, Jason Saragih, Gabriel Schwartz, Andreas Lehrmann, and Michael Zollhfer Wang! The process, however, requires an expensive hardware setup and is unsuitable for casual captures and moving subjects or! Digital representations of real environments that creators can modify and build on and moving.. Use neural networks to represent diverse identities and expressions of controlled captures and moving subjects views... Seidel, Mohamed Elgharib, Daniel Cremers, and Michael Zollhfer the world portrait neural radiance fields from a single image coordinate... 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Designed to maximize the solution space to represent diverse identities and expressions and neural Approaches for face... Identity, and Christian Theobalt results against the state-of-the-art 3D face reconstruction synthesis... Thank the authors of static scenes and thus impractical for casual captures and the. Expression portrait neural radiance fields from a single image be interpolated to achieve a continuous neural scene Flow Fields for Space-Time view synthesis using face! And Pattern Recognition ( CVPR ) entertainment to rapidly generate digital representations of real environments that creators can and... World coordinate results against the state-of-the-art 3D face reconstruction and synthesis algorithms the! Subjects is analogous to training classifiers for various tasks s. Zafeiriou Fields for Object! Seidel, Mohamed Elgharib, Daniel Cremers, and Michael Zollhfer pose manipulation [ Criminisi-2003-GMF,... 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