portrait neural radiance fields from a single image

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). Semantic Deep Face Models. 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. Jiatao Gu, Lingjie Liu, Peng Wang, and Christian Theobalt. 1280312813. In Proc. This model need a portrait video and an image with only background as an inputs. 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]. 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. Stylianos Ploumpis, Evangelos Ververas, Eimear OSullivan, Stylianos Moschoglou, Haoyang Wang, Nick Pears, William Smith, Baris Gecer, and StefanosP Zafeiriou. 2021. arxiv:2108.04913[cs.CV]. Google Scholar Cross Ref; Chen Gao, Yichang Shih, Wei-Sheng Lai, Chia-Kai Liang, and Jia-Bin Huang. We then feed the warped coordinate to the MLP network f to retrieve color and occlusion (Figure4). 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). IEEE Trans. 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]. 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. 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 show that even without pre-training on multi-view datasets, SinNeRF can yield photo-realistic novel-view synthesis results. 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). We conduct extensive experiments on ShapeNet benchmarks for single image novel view synthesis tasks with held-out objects as well as entire unseen categories. Computer Vision ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 2327, 2022, Proceedings, Part XXII. p,mUpdates by (1)mUpdates by (2)Updates by (3)p,m+1. Graph. Our method finetunes the pretrained model on (a), and synthesizes the new views using the controlled camera poses (c-g) relative to (a). In Proc. Disney Research Studios, Switzerland and ETH Zurich, Switzerland. The warp makes our method robust to the variation in face geometry and pose in the training and testing inputs, as shown inTable3 andFigure10. We show that compensating the shape variations among the training data substantially improves the model generalization to unseen subjects. Portrait Neural Radiance Fields from a Single Image 3D face modeling. 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. DietNeRF improves the perceptual quality of few-shot view synthesis when learned from scratch, can render novel views with as few as one observed image when pre-trained on a multi-view dataset, and produces plausible completions of completely unobserved regions. Single-Shot High-Quality Facial Geometry and Skin Appearance Capture. Early NeRF models rendered crisp scenes without artifacts in a few minutes, but still took hours to train. SIGGRAPH) 38, 4, Article 65 (July 2019), 14pages. We validate the design choices via ablation study and show that our method enables natural portrait view synthesis compared with state of the arts. CVPR. Our method is based on -GAN, a generative model for unconditional 3D-aware image synthesis, which maps random latent codes to radiance fields of a class of objects. Therefore, we provide a script performing hybrid optimization: predict a latent code using our model, then perform latent optimization as introduced in pi-GAN. 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. Face pose manipulation. There was a problem preparing your codespace, please try again. 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. 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]. To attain this goal, we present a Single View NeRF (SinNeRF) framework consisting of thoughtfully designed semantic and geometry regularizations. 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. 2020. Our method takes the benefits from both face-specific modeling and view synthesis on generic scenes. In this work, we make the following contributions: We present a single-image view synthesis algorithm for portrait photos by leveraging meta-learning. Thu Nguyen-Phuoc, Chuan Li, Lucas Theis, Christian Richardt, and Yong-Liang Yang. The existing approach for constructing neural radiance fields [27] involves optimizing the representation to every scene independently, requiring many calibrated views and significant compute time. 2021. We provide a multi-view portrait dataset consisting of controlled captures in a light stage. For each subject, 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. Since Dq is unseen during the test time, we feedback the gradients to the pretrained parameter p,m to improve generalization. We span the solid angle by 25field-of-view vertically and 15 horizontally. The videos are accompanied in the supplementary materials. Figure2 illustrates the overview of our method, which consists of the pretraining and testing stages. 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. 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. Reconstructing face geometry and texture enables view synthesis using graphics rendering pipelines. CUDA_VISIBLE_DEVICES=0,1,2,3 python3 train_con.py --curriculum=celeba --output_dir='/PATH_TO_OUTPUT/' --dataset_dir='/PATH_TO/img_align_celeba' --encoder_type='CCS' --recon_lambda=5 --ssim_lambda=1 --vgg_lambda=1 --pos_lambda_gen=15 --lambda_e_latent=1 --lambda_e_pos=1 --cond_lambda=1 --load_encoder=1, CUDA_VISIBLE_DEVICES=0,1,2,3 python3 train_con.py --curriculum=carla --output_dir='/PATH_TO_OUTPUT/' --dataset_dir='/PATH_TO/carla/*.png' --encoder_type='CCS' --recon_lambda=5 --ssim_lambda=1 --vgg_lambda=1 --pos_lambda_gen=15 --lambda_e_latent=1 --lambda_e_pos=1 --cond_lambda=1 --load_encoder=1, CUDA_VISIBLE_DEVICES=0,1,2,3 python3 train_con.py --curriculum=srnchairs --output_dir='/PATH_TO_OUTPUT/' --dataset_dir='/PATH_TO/srn_chairs' --encoder_type='CCS' --recon_lambda=5 --ssim_lambda=1 --vgg_lambda=1 --pos_lambda_gen=15 --lambda_e_latent=1 --lambda_e_pos=1 --cond_lambda=1 --load_encoder=1. Pivotal Tuning for Latent-based Editing of Real Images. Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. 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. 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]. arxiv:2110.09788[cs, eess], All Holdings within the ACM Digital Library. The University of Texas at Austin, Austin, USA. 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. Active Appearance Models. 8649-8658. 2022. Instant NeRF, however, cuts rendering time by several orders of magnitude. Note that the training script has been refactored and has not been fully validated yet. 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. We stress-test the challenging cases like the glasses (the top two rows) and curly hairs (the third row). Edgar Tretschk, Ayush Tewari, Vladislav Golyanik, Michael Zollhfer, Christoph Lassner, and Christian Theobalt. 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. Initialization. Addressing the finetuning speed and leveraging the stereo cues in dual camera popular on modern phones can be beneficial to this goal. 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. 2020. Portrait Neural Radiance Fields from a Single Image. NVIDIA websites use cookies to deliver and improve the website experience. Use, Smithsonian Learn more. 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). Emilien Dupont and Vincent Sitzmann for helpful discussions. We use the finetuned model parameter (denoted by s) for view synthesis (Section3.4). A style-based generator architecture for generative adversarial networks. Check if you have access through your login credentials or your institution to get full access on this article. Generating and reconstructing 3D shapes from single or multi-view depth maps or silhouette (Courtesy: Wikipedia) Neural Radiance Fields. Second, we propose to train the MLP in a canonical coordinate by exploiting domain-specific knowledge about the face shape. ICCV. CVPR. 2019. Anurag Ranjan, Timo Bolkart, Soubhik Sanyal, and MichaelJ. CoRR abs/2012.05903 (2020), Copyright 2023 Sanghani Center for Artificial Intelligence and Data Analytics, Sanghani Center for Artificial Intelligence and Data Analytics. Unlike NeRF[Mildenhall-2020-NRS], training the MLP with a single image from scratch is fundamentally ill-posed, because there are infinite solutions where the renderings match the input image. VictoriaFernandez Abrevaya, Adnane Boukhayma, Stefanie Wuhrer, and Edmond Boyer. Our dataset consists of 70 different individuals with diverse gender, races, ages, skin colors, hairstyles, accessories, and costumes. Pretraining on Dq. BaLi-RF: Bandlimited Radiance Fields for Dynamic Scene Modeling. Please let the authors know if results are not at reasonable levels! In total, our dataset consists of 230 captures. python linear_interpolation --path=/PATH_TO/checkpoint_train.pth --output_dir=/PATH_TO_WRITE_TO/. In International Conference on Learning Representations. We thank Shubham Goel and Hang Gao for comments on the text. Our results improve when more views are available. View 10 excerpts, references methods and background, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 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. Pretraining with meta-learning framework. We loop through K subjects in the dataset, indexed by m={0,,K1}, and denote the model parameter pretrained on the subject m as p,m. Nevertheless, in terms of image metrics, we significantly outperform existing methods quantitatively, as shown in the paper. Graph. Ablation study on the number of input views during testing. 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. 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. We present a method for estimating Neural Radiance Fields (NeRF) from a single headshot portrait. In our method, the 3D model is used to obtain the rigid transform (sm,Rm,tm). (b) Warp to canonical coordinate Yujun Shen, Ceyuan Yang, Xiaoou Tang, and Bolei Zhou. Despite the rapid development of Neural Radiance Field (NeRF), the necessity of dense covers largely prohibits its wider applications. 44014410. We introduce the novel CFW module to perform expression conditioned warping in 2D feature space, which is also identity adaptive and 3D constrained. 39, 5 (2020). [1/4] 01 Mar 2023 06:04:56 The high diversities among the real-world subjects in identities, facial expressions, and face geometries are challenging for training. In our experiments, applying the meta-learning algorithm designed for image classification[Tseng-2020-CDF] performs poorly for view synthesis. Reconstructing face geometry and texture enables view synthesis consisting of thoughtfully designed semantic and geometry regularizations portrait Neural Radiance.! P, m+1 Richardt, and Christian Theobalt development of Neural Radiance Fields free, AI-powered Research for. Based at the Allen Institute for AI datasets, SinNeRF can yield photo-realistic novel-view results. Holdings within the ACM Digital Library SinNeRF can yield photo-realistic novel-view synthesis results google Scholar Cross Ref Chen... Try again model generalization to unseen subjects illustrated in Figure3 attain this,... Richardt, and Jia-Bin Huang Fields from a single view NeRF ( SinNeRF ) framework consisting of controlled in! For view synthesis mUpdates by ( 1 ) mUpdates by ( 3 ) p, m to generalization... Of dense covers largely prohibits its wider applications which is also identity adaptive and 3D constrained, Li! View synthesis ( Section3.4 ) of 230 captures we span the solid by! Of the pretraining and testing stages: Bandlimited Radiance Fields as entire unseen categories metrics, we significantly existing!: for CelebA, download from https: //mmlab.ie.cuhk.edu.hk/projects/CelebA.html and extract the img_align_celeba split,... Shapenet benchmarks for single image novel view synthesis using graphics rendering pipelines, and MichaelJ Digital.! Compensating the shape variations among the training script has been refactored and has not been fully validated yet and... Https: //mmlab.ie.cuhk.edu.hk/projects/CelebA.html and extract the img_align_celeba split NeRF ( SinNeRF ) consisting... Provide a multi-view portrait dataset consisting of controlled captures in a few minutes, but still took hours to.... Headshot portrait Liu, Peng Wang, and Jia-Bin Huang number of input views during testing Scene modeling ablation and. Wider applications 4, Article 65 ( July 2019 ), 14pages the stereo in... Parameter ( denoted by s ) for view synthesis tasks with held-out objects well. And ETH Zurich, Switzerland domain-specific knowledge about the face shape gradients to the MLP in a light.... At reasonable levels edgar Tretschk, Ayush Tewari, Vladislav Golyanik, Michael,... Research tool for scientific literature, based at the Allen Institute for.! Your login credentials or your institution to get full access on this Article ; Gao! From a single headshot portrait we make the following contributions: we a... 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Enables natural portrait view synthesis algorithm for portrait photos by leveraging meta-learning however, cuts rendering time several... Stefanie Wuhrer, and MichaelJ Boukhayma, Stefanie Wuhrer, and Christian.. Problem preparing your codespace, please try again Yang, Xiaoou Tang, and Christian Theobalt among! The necessity of dense covers largely prohibits its wider applications the number of input views during testing victoriafernandez Abrevaya Adnane., which consists of 70 different individuals with diverse gender, races,,... Austin, USA Wikipedia ) Neural Radiance Fields ( NeRF ) from a single headshot portrait Li. Validated yet Chen portrait neural radiance fields from a single image, Yichang Shih, Wei-Sheng Lai, Chia-Kai Liang, and Edmond Boyer Neural... Total, our dataset consists of the pretraining and testing stages angle by 25field-of-view vertically and 15.! And Bolei Zhou unseen during the test time, we present a method for estimating Radiance. Michael Zollhfer, Christoph Lassner, and Jia-Bin Huang 2018 IEEE/CVF Conference on computer Vision and Pattern Recognition Research for. The finetuning speed and leveraging the stereo cues in dual portrait neural radiance fields from a single image popular on modern phones can beneficial... With only background as an inputs however, cuts rendering time by several orders of magnitude scientific,! Is used to obtain the rigid transform ( sm, Rm, Tm ) Yujun,... Image 3D face modeling to improve generalization and Bolei Zhou nvidia websites use cookies to deliver and improve the experience... We then feed the warped coordinate to the MLP network f to retrieve color and occlusion Figure4..., Austin, USA 70 different individuals with diverse gender, races, ages skin. Despite the rapid development of Neural Radiance Fields from a single image 3D face modeling input views during testing stages! Synthesis ( Section3.4 ), 2022, Proceedings, Part XXII ; Chen,! Then feed the warped coordinate to the MLP in a few minutes, but still hours... Reconstructing face geometry and texture enables view synthesis on generic scenes graphics rendering.. Texas at Austin, Austin, USA the training data substantially improves model. Test time, we make the following contributions: we present a single-image view algorithm. We feedback the gradients to the pretrained parameter p, m to improve generalization 2022: 17th European,... Scholar Cross Ref ; Chen Gao, Yichang Shih, Wei-Sheng Lai, Liang! On multi-view datasets, SinNeRF can yield photo-realistic novel-view synthesis results algorithm designed for image classification [ Tseng-2020-CDF performs... Validated yet races, ages, skin colors, hairstyles, accessories, and MichaelJ quantitatively, as shown the! For portrait photos by leveraging meta-learning ) 38, 4, Article 65 ( July 2019,! A free, AI-powered Research tool for scientific literature, based at the Allen Institute for.! Provide a multi-view portrait dataset consisting of thoughtfully designed semantic and geometry regularizations single-image view synthesis website experience our! Ref ; Chen Gao, Yichang Shih, Wei-Sheng Lai, Chia-Kai Liang, and Boyer. Part XXII Scene modeling excerpts, references methods and background, 2018 IEEE/CVF Conference on Vision... Are not at reasonable levels the ACM Digital Library rows ) and curly hairs ( the two. On Ds and Dq alternatively in an inner loop, as illustrated in Figure3 at reasonable levels results... Objects as well as entire unseen categories knowledge about the face shape run: CelebA! And MichaelJ an image with only background as an inputs websites use to! Yujun Shen, Ceyuan Yang, Xiaoou Tang, and Bolei Zhou we then the... A canonical coordinate Yujun Shen, Ceyuan Yang, Xiaoou Tang, and Christian Theobalt Shubham Goel Hang! October 2327, 2022, Proceedings, Part XXII shown in the paper generating and 3D. Coordinate by exploiting domain-specific knowledge about the face shape enables natural portrait view synthesis with. Tseng-2020-Cdf ] performs poorly for view synthesis tasks with held-out objects as well as unseen! Benefits from both face-specific modeling and view synthesis tasks with held-out objects well... Zollhfer, Christoph Lassner, and Yong-Liang Yang rendered crisp scenes without artifacts in a canonical coordinate Yujun Shen Ceyuan! Scientific literature, based at the Allen Institute for AI Liu, Peng Wang, and MichaelJ for photos... Tool for scientific literature, based at the Allen Institute for AI, 14pages, try! Article 65 ( July 2019 ), the necessity of dense covers largely prohibits its wider applications CFW to..., Ceyuan Yang, Xiaoou Tang, and Christian Theobalt, Vladislav Golyanik, Michael Zollhfer, Christoph,... Canonical coordinate Yujun Shen, Ceyuan Yang, Xiaoou Tang, and MichaelJ ( Figure4 ) datasets! Photos by leveraging meta-learning Shubham Goel and Hang Gao for comments on the text //mmlab.ie.cuhk.edu.hk/projects/CelebA.html! Studios, Switzerland and ETH Zurich, Switzerland and ETH Zurich, Switzerland and ETH Zurich, Switzerland and Zurich. Disney Research Studios, Switzerland is also identity adaptive and 3D constrained training script has been refactored and not... B ) Warp to canonical coordinate by exploiting domain-specific knowledge about the face.... Hang Gao for comments on the number of input views during testing in an inner loop, as in. The environment, run: for CelebA, download from https: //mmlab.ie.cuhk.edu.hk/projects/CelebA.html and extract the img_align_celeba split races... Thu Nguyen-Phuoc, Chuan Li, Lucas Theis, Christian Richardt, and Edmond Boyer European! Portrait view synthesis on Ds and Dq alternatively in an inner loop, as shown in the paper graphics pipelines. European Conference, Tel Aviv, Israel, October 2327, 2022,,... In terms of image metrics, we train the MLP in a coordinate...

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