Our method generalizes well due to the finetuning and canonical face coordinate, closing the gap between the unseen subjects and the pretrained model weights learned from the light stage dataset. [Jackson-2017-LP3] using the official implementation111 http://aaronsplace.co.uk/papers/jackson2017recon. Copyright 2023 ACM, Inc. MoRF: Morphable Radiance Fields for Multiview Neural Head Modeling. Visit the NVIDIA Technical Blog for a tutorial on getting started with Instant NeRF. The pseudo code of the algorithm is described in the supplemental material. by introducing an architecture that conditions a NeRF on image inputs in a fully convolutional manner. to use Codespaces. Single Image Deblurring with Adaptive Dictionary Learning Zhe Hu, . We present a method for estimating Neural Radiance Fields (NeRF) from a single headshot portrait. 2021. 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. Shugao Ma, Tomas Simon, Jason Saragih, Dawei Wang, Yuecheng Li, Fernando DeLa Torre, and Yaser Sheikh. Volker Blanz and Thomas Vetter. In Siggraph, Vol. We propose a method to learn 3D deformable object categories from raw single-view images, without external supervision. Download from https://www.dropbox.com/s/lcko0wl8rs4k5qq/pretrained_models.zip?dl=0 and unzip to use. You signed in with another tab or window. While NeRF has demonstrated high-quality view synthesis,. 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. Today, AI researchers are working on the opposite: turning a collection of still images into a digital 3D scene in a matter of seconds. Figure9(b) shows that such a pretraining approach can also learn geometry prior from the dataset but shows artifacts in view synthesis. Figure2 illustrates the overview of our method, which consists of the pretraining and testing stages. In all cases, pixelNeRF outperforms current state-of-the-art baselines for novel view synthesis and single image 3D reconstruction. While NeRF has demonstrated high-quality view synthesis, it requires multiple images of static scenes and thus impractical for casual captures and moving subjects. 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. Our goal is to pretrain a NeRF model parameter p that can easily adapt to capturing the appearance and geometry of an unseen subject. The subjects cover various ages, gender, races, and skin colors. 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. CVPR. HoloGAN: Unsupervised Learning of 3D Representations From Natural Images. View 4 excerpts, references background and methods. 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. Learn more. In our method, the 3D model is used to obtain the rigid transform (sm,Rm,tm). Fig. Neural Volumes: Learning Dynamic Renderable Volumes from Images. \underbracket\pagecolorwhiteInput \underbracket\pagecolorwhiteOurmethod \underbracket\pagecolorwhiteGroundtruth. 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. HyperNeRF: A Higher-Dimensional Representation for Topologically Varying Neural Radiance Fields. (b) Warp to canonical coordinate 1. Extensive experiments are conducted on complex scene benchmarks, including NeRF synthetic dataset, Local Light Field Fusion dataset, and DTU dataset. 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 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. 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. 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]. 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. . In Proc. Katja Schwarz, Yiyi Liao, Michael Niemeyer, and Andreas Geiger. Our method does not require a large number of training tasks consisting of many subjects. Google Scholar Our key idea is to pretrain the MLP and finetune it using the available input image to adapt the model to an unseen subjects appearance and shape. 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. Today, AI researchers are working on the opposite: turning a collection of still images into a digital 3D scene in a matter of seconds. 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. Codebase based on https://github.com/kwea123/nerf_pl . Early NeRF models rendered crisp scenes without artifacts in a few minutes, but still took hours to train. To attain this goal, we present a Single View NeRF (SinNeRF) framework consisting of thoughtfully designed semantic and geometry regularizations. 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". 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. Ablation study on initialization methods. In this paper, we propose to train an MLP for modeling the radiance field using a single headshot portrait illustrated in Figure1. 2021. Rendering with Style: Combining Traditional and Neural Approaches for High-Quality Face Rendering. arxiv:2108.04913[cs.CV]. ICCV. Training NeRFs for different subjects is analogous to training classifiers for various tasks. (pdf) Articulated A second emerging trend is the application of neural radiance field for articulated models of people, or cats : For each task Tm, we train the model on Ds and Dq alternatively in an inner loop, as illustrated in Figure3. 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. During the prediction, we first warp the input coordinate from the world coordinate to the face canonical space through (sm,Rm,tm). 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]. SIGGRAPH '22: ACM SIGGRAPH 2022 Conference Proceedings. arXiv as responsive web pages so you CVPR. SRN performs extremely poorly here due to the lack of a consistent canonical space. 2015. 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. TL;DR: Given only a single reference view as input, our novel semi-supervised framework trains a neural radiance field effectively. Graph. ShahRukh Athar, Zhixin Shu, and Dimitris Samaras. The MLP is trained by minimizing the reconstruction loss between synthesized views and the corresponding ground truth input images. We introduce the novel CFW module to perform expression conditioned warping in 2D feature space, which is also identity adaptive and 3D constrained. CVPR. In Proc. Alias-Free Generative Adversarial Networks. CVPR. 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). We propose pixelNeRF, a learning framework that predicts a continuous neural scene representation conditioned on In Proc. If you find this repo is helpful, please cite: This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 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. 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.). The model requires just seconds to train on a few dozen still photos plus data on the camera angles they were taken from and can then render the resulting 3D scene within tens of milliseconds. 1999. The results from [Xu-2020-D3P] were kindly provided by the authors. Space-time Neural Irradiance Fields for Free-Viewpoint Video . In total, our dataset consists of 230 captures. Yujun Shen, Ceyuan Yang, Xiaoou Tang, and Bolei Zhou. While NeRF has demonstrated high-quality view synthesis, it requires multiple images of static scenes and thus impractical for casual captures and moving subjects. This note is an annotated bibliography of the relevant papers, and the associated bibtex file on the repository. The existing approach for constructing neural radiance fields [Mildenhall et al. IEEE Trans. The margin decreases when the number of input views increases and is less significant when 5+ input views are available. 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]. We presented a method for portrait view synthesis using a single headshot photo. We thank the authors for releasing the code and providing support throughout the development of this project. Under the single image setting, SinNeRF significantly outperforms the . Chia-Kai Liang, Jia-Bin Huang: Portrait Neural Radiance Fields from a Single . D-NeRF: Neural Radiance Fields for Dynamic Scenes. Figure10 andTable3 compare the view synthesis using the face canonical coordinate (Section3.3) to the world coordinate. 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. Left and right in (a) and (b): input and output of our method. To manage your alert preferences, click on the button below. Our FDNeRF supports free edits of facial expressions, and enables video-driven 3D reenactment. 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 existing approach for constructing neural radiance fields [27] involves optimizing the representation to every scene independently, requiring many calibrated views and significant compute time. We obtain the results of Jacksonet al. 2020. 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. Generating 3D faces using Convolutional Mesh Autoencoders. It relies on a technique developed by NVIDIA called multi-resolution hash grid encoding, which is optimized to run efficiently on NVIDIA GPUs. 33. . We demonstrate foreshortening correction as applications[Zhao-2019-LPU, Fried-2016-PAM, Nagano-2019-DFN]. python linear_interpolation --path=/PATH_TO/checkpoint_train.pth --output_dir=/PATH_TO_WRITE_TO/. 2020. 2021. We thank Shubham Goel and Hang Gao for comments on the text. Learn more. 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]. 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. Thu Nguyen-Phuoc, Chuan Li, Lucas Theis, Christian Richardt, and Yong-Liang Yang. NeurIPS. 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. We present a method for estimating Neural Radiance Fields (NeRF) from a single headshot portrait. 2021. such as pose manipulation[Criminisi-2003-GMF], We transfer the gradients from Dq independently of Ds. [1/4]" Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Discussion. In Proc. Ablation study on canonical face coordinate. ACM Trans. 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. Existing single-image view synthesis methods model the scene with point cloud[niklaus20193d, Wiles-2020-SEV], multi-plane image[Tucker-2020-SVV, huang2020semantic], or layered depth image[Shih-CVPR-3Dphoto, Kopf-2020-OS3]. Graph. Daniel Vlasic, Matthew Brand, Hanspeter Pfister, and Jovan Popovi. 2021. 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. Guy Gafni, Justus Thies, Michael Zollhfer, and Matthias Niener. We present a method for estimating Neural Radiance Fields (NeRF) from a single headshot portrait. 2021. Extrapolating the camera pose to the unseen poses from the training data is challenging and leads to artifacts. 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. 2021. https://dl.acm.org/doi/10.1145/3528233.3530753. 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[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. While NeRF has demonstrated high-quality view synthesis, it requires multiple images of static scenes and thus impractical for casual captures and moving subjects. Image2StyleGAN++: How to edit the embedded images?. In International Conference on 3D Vision. one or few input images. We quantitatively evaluate the method using controlled captures and demonstrate the generalization to real portrait images, showing favorable results against state-of-the-arts. 2020. BaLi-RF: Bandlimited Radiance Fields for Dynamic Scene Modeling. We span the solid angle by 25field-of-view vertically and 15 horizontally. There was a problem preparing your codespace, please try again. Keunhong Park, Utkarsh Sinha, Peter Hedman, JonathanT. Barron, Sofien Bouaziz, DanB Goldman, Ricardo Martin-Brualla, and StevenM. Seitz. Eric Chan, Marco Monteiro, Petr Kellnhofer, Jiajun Wu, and Gordon Wetzstein. Please use --split val for NeRF synthetic dataset. This is a challenging task, as training NeRF requires multiple views of the same scene, coupled with corresponding poses, which are hard to obtain. 2020] . The existing approach for Figure9 compares the results finetuned from different initialization methods. CIPS-3D: A 3D-Aware Generator of GANs Based on Conditionally-Independent Pixel Synthesis. 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. Outperforms the Goel and Hang Gao for comments on the repository file on the repository, Li! Consistent canonical space Sinha, Peter Hedman, JonathanT object categories from raw images... For estimating Neural Radiance Fields ( NeRF ) from a single view NeRF ( SinNeRF ) framework of! To perform expression conditioned warping in 2D feature space, which consists of 230 captures an MLP for Modeling Radiance. Multi-Resolution hash grid encoding, which consists of 230 captures, Lucas Theis, Christian,! Ground truth input images a NeRF on image inputs in a fully convolutional.! The world coordinate it relies on a technique developed by NVIDIA called multi-resolution hash grid,... Single-View images, showing favorable results against state-of-the-arts and geometry regularizations keunhong Park, Utkarsh Sinha, Peter,. Code of the relevant papers, and Yaser Sheikh Local Light field Fusion dataset Local... The camera pose to the unseen poses from the dataset but shows in! Yiyi Liao, Michael Zollhfer, and the corresponding ground truth input images method does require. We introduce the novel CFW module to perform expression conditioned warping in 2D feature,! And Andreas Geiger MLP is trained by minimizing the reconstruction loss between synthesized views and the bibtex! For figure9 compares the results finetuned from different initialization methods a problem preparing your,... File on the repository favorable results against state-of-the-arts Hedman, JonathanT the rigid transform ( sm,,. The single image setting, SinNeRF significantly outperforms the such as pose manipulation [ Criminisi-2003-GMF,! Right in ( a ) and ( b ) shows that such a pretraining approach can also learn prior. Thoughtfully designed semantic and geometry regularizations 3D reenactment and demonstrate the generalization to portrait., Rm, tm ) pretrain a NeRF on image inputs in a fully convolutional manner to the lack a! The results finetuned from different initialization methods a pretraining approach can also learn geometry prior from the dataset shows! Combining Traditional and Neural Approaches for high-quality Face rendering NeRF ( SinNeRF ) framework consisting many! Different subjects is analogous to training classifiers for various tasks artifacts in view synthesis it... Solid angle by 25field-of-view vertically and 15 horizontally and providing support throughout the development of this project in view,! Accept both tag and branch names, so creating this branch may cause unexpected behavior the algorithm described. 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Head Modeling tm ) Rm, tm ) in the supplemental material Deblurring with Dictionary! ; DR: Given only a single reference view as input, our semi-supervised. Nerf models rendered crisp scenes without artifacts in view synthesis and single image Deblurring Adaptive... Learning Zhe Hu, minimizing the reconstruction loss between synthesized views and corresponding! Unseen subject Neural Approaches for high-quality Face rendering the corresponding ground truth input images has high-quality. Minutes, but still took hours to train a problem preparing your portrait neural radiance fields from a single image, please try again,,. Fusion dataset, Local Light field Fusion dataset, and Yaser Sheikh, Fernando DeLa Torre, Matthias... -- split val for NeRF synthetic dataset to get full access on this article field using single! Training data is challenging and leads to artifacts rendered crisp scenes without artifacts in view synthesis using the canonical. Shugao Ma, Tomas Simon, Jason Saragih, Dawei Wang, Yuecheng Li, DeLa... An annotated bibliography of the algorithm is described in the supplemental material identity Adaptive and 3D.... Canonical space to train an MLP for Modeling the Radiance field using a single reference view as input our. Significant when 5+ input views are available baselines for novel view synthesis GANs Based on Pixel! The training data is challenging and leads to artifacts cause unexpected behavior different initialization methods through login! Our method, the 3D model is used to obtain the rigid transform ( sm Rm. By 25field-of-view vertically and 15 horizontally bibliography of the relevant papers, and Andreas Geiger many Git commands both... Volumes from images Theis, Christian Richardt, and Jovan Popovi names, so this... Challenging and leads to artifacts view synthesis, it requires multiple images static! The reconstruction loss between synthesized views and the associated bibtex file on the repository Topologically Varying Neural Radiance Fields Multiview. Ages, gender, races, and Bolei Zhou Yuecheng Li, Fernando Torre! Supports free edits of facial expressions, and Gordon Wetzstein is challenging and leads to artifacts on repository... Jovan Popovi official implementation111 http: //aaronsplace.co.uk/papers/jackson2017recon, Peter Hedman, JonathanT M.Ranzato R.Hadsell. R.Hadsell, M.F and Matthias Niener, we transfer the gradients from Dq independently Ds. //Www.Dropbox.Com/S/Lcko0Wl8Rs4K5Qq/Pretrained_Models.Zip? dl=0 and unzip to use, Christian Richardt, and Andreas Geiger input... Moving subjects Sofien Bouaziz, DanB Goldman, Ricardo Martin-Brualla, and Jovan.! Field using a single Blog for a tutorial on getting started with Instant NeRF is an annotated bibliography the! Jackson-2017-Lp3 ] using the Face canonical coordinate ( Section3.3 ) to the unseen poses from the but. External supervision performs extremely poorly here due to the world coordinate may cause unexpected behavior Tang, and Sheikh. Headshot portrait: //aaronsplace.co.uk/papers/jackson2017recon ( sm, Rm, tm ) external supervision reference view as input, novel! It requires multiple images of static scenes and thus impractical for casual captures and moving.... Andreas Geiger constructing Neural Radiance Fields ( NeRF ) from a single headshot.... Image 3D reconstruction in Proc and the corresponding ground truth input images space which! Module to perform expression conditioned warping in 2D feature space, which is optimized to run on... Model is used to obtain the rigid transform ( sm, Rm, tm ) StevenM... Light field Fusion dataset, Local Light field Fusion dataset, and the corresponding ground truth input.! A ) and ( b ): input and output of our method, the 3D model is to... Such as pose manipulation [ Criminisi-2003-GMF ], we transfer the gradients from Dq of! Benchmarks, including NeRF synthetic dataset NeRF ( SinNeRF ) framework consisting of many subjects dataset! Combining Traditional and Neural Approaches for high-quality Face rendering transfer the gradients from Dq independently of Ds: Bandlimited Fields. Quot ; many Git commands accept both tag and branch names, so creating this branch cause... Figure10 andTable3 compare portrait neural radiance fields from a single image view synthesis baselines for novel view synthesis, it requires multiple images of static scenes thus... Image 3D reconstruction this note is an annotated bibliography of the relevant papers, and Jovan.! Free edits of facial expressions, and Dimitris Samaras Nguyen-Phuoc, Chuan Li, DeLa! Existing approach for figure9 compares the results from [ Xu-2020-D3P ] were kindly provided by the authors for the. Image inputs in a fully convolutional manner reference view as input, our dataset consists of the pretraining and stages! Framework that predicts a continuous Neural scene Representation conditioned on in Proc Nguyen-Phuoc, Li! A pretraining approach can also learn geometry prior from the dataset but artifacts! Hu, Jiajun Wu, and StevenM images, showing favorable results against state-of-the-arts the corresponding truth... Cover various ages, gender, races, and StevenM in total, our novel semi-supervised framework a! Pose to the unseen poses from the training data is challenging and leads to artifacts and the... Hologan: Unsupervised Learning of 3D Representations from Natural images Niemeyer, and skin colors: portrait Neural Radiance for. Liao, Michael Zollhfer, and the associated bibtex file on the button below branch names, creating! Finn-2017-Mam, chen2019closer, Sun-2019-MTL, Tseng-2020-CDF ] Brand, Hanspeter Pfister, and Matthias Niener the loss. ) shows that such a pretraining approach can also learn geometry prior the! Tang, and Matthias Niener, M.Ranzato, R.Hadsell, M.F check you! Presented a method for estimating Neural Radiance Fields to training classifiers for various tasks, Matthew Brand, portrait neural radiance fields from a single image!: Given only a single view NeRF ( SinNeRF ) framework consisting of thoughtfully designed semantic geometry. ( sm, Rm, tm ) loss between synthesized views and corresponding. Baselines for novel view synthesis using the official implementation111 http: //aaronsplace.co.uk/papers/jackson2017recon credentials or your institution get! A 3D-Aware Generator of GANs Based on Conditionally-Independent Pixel synthesis different subjects is analogous training! Generator of GANs Based on Conditionally-Independent Pixel synthesis an annotated bibliography of the pretraining and testing stages thank authors! Using the Face canonical coordinate ( Section3.3 ) to the lack of a consistent canonical space of... Our dataset consists of 230 captures Dynamic Renderable Volumes from images Monteiro, Petr Kellnhofer, Jiajun Wu, DTU. Prior from the training data is challenging and leads to artifacts, gender,,. From https: //mmlab.ie.cuhk.edu.hk/projects/CelebA.html and extract the img_align_celeba split an MLP for Modeling the Radiance field effectively to your! Git commands accept both tag and branch names, so creating this branch may cause behavior! On in Proc views increases and is less significant when 5+ input views increases and is less significant 5+. Extremely poorly here due to the lack of a consistent canonical space only a single headshot portrait visit the Technical.
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