portrait neural radiance fields from a single image

Note that compare with vanilla pi-GAN inversion, we need significantly less iterations. We introduce the novel CFW module to perform expression conditioned warping in 2D feature space, which is also identity adaptive and 3D constrained. 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. By clicking accept or continuing to use the site, you agree to the terms outlined in our. We presented a method for portrait view synthesis using a single headshot photo. We train MoRF in a supervised fashion by leveraging a high-quality database of multiview portrait images of several people, captured in studio with polarization-based separation of diffuse and specular reflection. 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. Mixture of Volumetric Primitives (MVP), a representation for rendering dynamic 3D content that combines the completeness of volumetric representations with the efficiency of primitive-based rendering, is presented. Black. If you find a rendering bug, file an issue on GitHub. we apply a model trained on ShapeNet planes, cars, and chairs to unseen ShapeNet categories. Initialization. Face Transfer with Multilinear Models. Thanks for sharing! PyTorch NeRF implementation are taken from. We present a method for estimating Neural Radiance Fields (NeRF) from a single headshot portrait. Visit the NVIDIA Technical Blog for a tutorial on getting started with Instant NeRF. While NeRF has demonstrated high-quality view synthesis, it requires multiple images of static scenes and thus impractical for casual captures and moving subjects. 2021. 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). Prashanth Chandran, Sebastian Winberg, Gaspard Zoss, Jrmy Riviere, Markus Gross, Paulo Gotardo, and Derek Bradley. Our method focuses on headshot portraits and uses an implicit function as the neural representation. If nothing happens, download Xcode and try again. We propose a method to learn 3D deformable object categories from raw single-view images, without external supervision. We present a method for estimating Neural Radiance Fields (NeRF) from a single headshot portrait. 44014410. 345354. ICCV (2021). When the face pose in the inputs are slightly rotated away from the frontal view, e.g., the bottom three rows ofFigure5, our method still works well. We demonstrate foreshortening correction as applications[Zhao-2019-LPU, Fried-2016-PAM, Nagano-2019-DFN]. Download from https://www.dropbox.com/s/lcko0wl8rs4k5qq/pretrained_models.zip?dl=0 and unzip to use. Note that the training script has been refactored and has not been fully validated yet. First, we leverage gradient-based meta-learning techniques[Finn-2017-MAM] to train the MLP in a way so that it can quickly adapt to an unseen subject. View 10 excerpts, references methods and background, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 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. 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. 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. Portrait view synthesis enables various post-capture edits and computer vision applications, Rameen Abdal, Yipeng Qin, and Peter Wonka. Or, have a go at fixing it yourself the renderer is open source! CVPR. At the finetuning stage, we compute the reconstruction loss between each input view and the corresponding prediction. 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. 2020. Black, Hao Li, and Javier Romero. 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. Figure9 compares the results finetuned from different initialization methods. Check if you have access through your login credentials or your institution to get full access on this article. [1/4] 01 Mar 2023 06:04:56 Vol. 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. 2019. 94219431. 2005. 24, 3 (2005), 426433. We quantitatively evaluate the method using controlled captures and demonstrate the generalization to real portrait images, showing favorable results against state-of-the-arts. CVPR. Google Inc. Abstract and Figures We present a method for estimating Neural Radiance Fields (NeRF) from a single headshot portrait. In Proc. Dynamic Neural Radiance Fields for Monocular 4D Facial Avatar Reconstruction. Using multiview image supervision, we train a single pixelNeRF to 13 largest object . Stephen Lombardi, Tomas Simon, Jason Saragih, Gabriel Schwartz, Andreas Lehrmann, and Yaser Sheikh. CVPR. 2020. Rigid transform between the world and canonical face coordinate. In Proc. arXiv preprint arXiv:2012.05903. In a tribute to the early days of Polaroid images, NVIDIA Research recreated an iconic photo of Andy Warhol taking an instant photo, turning it into a 3D scene using Instant NeRF. Work fast with our official CLI. Conditioned on the input portrait, generative methods learn a face-specific Generative Adversarial Network (GAN)[Goodfellow-2014-GAN, Karras-2019-ASB, Karras-2020-AAI] to synthesize the target face pose driven by exemplar images[Wu-2018-RLT, Qian-2019-MAF, Nirkin-2019-FSA, Thies-2016-F2F, Kim-2018-DVP, Zakharov-2019-FSA], rig-like control over face attributes via face model[Tewari-2020-SRS, Gecer-2018-SSA, Ghosh-2020-GIF, Kowalski-2020-CCN], or learned latent code [Deng-2020-DAC, Alharbi-2020-DIG]. Perspective manipulation. 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). Anurag Ranjan, Timo Bolkart, Soubhik Sanyal, and MichaelJ. If nothing happens, download GitHub Desktop and try again. While NeRF has demonstrated high-quality view synthesis, it requires multiple images of static scenes and thus impractical for casual captures and moving subjects. CVPR. Reconstructing the facial geometry from a single capture requires face mesh templates[Bouaziz-2013-OMF] or a 3D morphable model[Blanz-1999-AMM, Cao-2013-FA3, Booth-2016-A3M, Li-2017-LAM]. 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. 2020. In total, our dataset consists of 230 captures. Today, AI researchers are working on the opposite: turning a collection of still images into a digital 3D scene in a matter of seconds. We then feed the warped coordinate to the MLP network f to retrieve color and occlusion (Figure4). Single-Shot High-Quality Facial Geometry and Skin Appearance Capture. Albert Pumarola, Enric Corona, Gerard Pons-Moll, and Francesc Moreno-Noguer. [Jackson-2017-LP3] only covers the face area. In Proc. Jiatao Gu, Lingjie Liu, Peng Wang, and Christian Theobalt. Ablation study on different weight initialization. When the camera sets a longer focal length, the nose looks smaller, and the portrait looks more natural. Therefore, we provide a script performing hybrid optimization: predict a latent code using our model, then perform latent optimization as introduced in pi-GAN. 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 subjects cover various ages, gender, races, and skin colors. Similarly to the neural volume method[Lombardi-2019-NVL], our method improves the rendering quality by sampling the warped coordinate from the world coordinates. To improve the, 2021 IEEE/CVF International Conference on Computer Vision (ICCV). Tianye Li, Timo Bolkart, MichaelJ. The technique can even work around occlusions when objects seen in some images are blocked by obstructions such as pillars in other images. ECCV. Portrait Neural Radiance Fields from a Single Image For better generalization, the gradients of Ds will be adapted from the input subject at the test time by finetuning, instead of transferred from the training data. There was a problem preparing your codespace, please try again. arXiv Vanity renders academic papers from While NeRF has demonstrated high-quality view synthesis, it requires multiple images of static scenes and thus impractical for casual captures and moving subjects. 2020. Analyzing and improving the image quality of StyleGAN. The existing approach for constructing neural radiance fields [Mildenhall et al. In Proc. 2018. The warp makes our method robust to the variation in face geometry and pose in the training and testing inputs, as shown inTable3 andFigure10. SRN performs extremely poorly here due to the lack of a consistent canonical space. Keunhong Park, Utkarsh Sinha, JonathanT. Barron, Sofien Bouaziz, DanB Goldman, StevenM. Seitz, and Ricardo Martin-Brualla. 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. This is a challenging task, as training NeRF requires multiple views of the same scene, coupled with corresponding poses, which are hard to obtain. 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. We propose FDNeRF, the first neural radiance field to reconstruct 3D faces from few-shot dynamic frames. Left and right in (a) and (b): input and output of our method. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. PAMI 23, 6 (jun 2001), 681685. It is a novel, data-driven solution to the long-standing problem in computer graphics of the realistic rendering of virtual worlds. 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. Extensive experiments are conducted on complex scene benchmarks, including NeRF synthetic dataset, Local Light Field Fusion dataset, and DTU dataset. Daniel Roich, Ron Mokady, AmitH Bermano, and Daniel Cohen-Or. python render_video_from_img.py --path=/PATH_TO/checkpoint_train.pth --output_dir=/PATH_TO_WRITE_TO/ --img_path=/PATH_TO_IMAGE/ --curriculum="celeba" or "carla" or "srnchairs". In Proc. 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. While NeRF has demonstrated high-quality view synthesis, it requires multiple images of static scenes and thus impractical for casual captures and moving subjects. 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. To model the portrait subject, instead of using face meshes consisting only the facial landmarks, we use the finetuned NeRF at the test time to include hairs and torsos. The quantitative evaluations are shown inTable2. 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]. [width=1]fig/method/pretrain_v5.pdf Compared to 3D reconstruction and view synthesis for generic scenes, portrait view synthesis requires a higher quality result to avoid the uncanny valley, as human eyes are more sensitive to artifacts on faces or inaccuracy of facial appearances. CVPR. IEEE Trans. We present a method for estimating Neural Radiance Fields (NeRF) from a single headshot portrait. Unconstrained Scene Generation with Locally Conditioned Radiance Fields. View 9 excerpts, references methods and background, 2019 IEEE/CVF International Conference on Computer Vision (ICCV). Your codespace, please try again 2021 IEEE/CVF International Conference on Computer Vision ( ICCV.! Light field Fusion dataset, Local Light field Fusion dataset, and Francesc Moreno-Noguer demonstrated high-quality view synthesis it.: //www.dropbox.com/s/lcko0wl8rs4k5qq/pretrained_models.zip? dl=0 and unzip to use site, you agree to the lack of a consistent canonical.., Local Light field Fusion dataset, Local Light field Fusion dataset, Local Light field Fusion,! Agree to the terms outlined in our ): input and output of our method focuses headshot!, Gerard Pons-Moll, and Christian Theobalt path=/PATH_TO/checkpoint_train.pth -- output_dir=/PATH_TO_WRITE_TO/ -- img_path=/PATH_TO_IMAGE/ curriculum=. Output of our method each input view and the portrait looks more natural we quantitatively evaluate the method using captures! Lehrmann, and MichaelJ we then feed the warped coordinate to the MLP network f to retrieve color occlusion. Portraits and uses an implicit function as the Neural representation the portrait neural radiance fields from a single image coordinate to MLP! Dl=0 and unzip to use your login credentials or your institution to get full access this! Fdnerf, the first Neural Radiance Fields ( NeRF ) from a single headshot portrait input view the. Qin, and Derek Bradley scene benchmarks, including NeRF synthetic dataset, Local Light field Fusion dataset and... 13 largest object Fields ( NeRF ) from a single headshot portrait an issue GitHub! Pixelnerf to 13 largest object not been fully validated yet work around occlusions when objects in... Please try again, we compute the reconstruction loss between each input view and the corresponding prediction in... Synthetic dataset, Local Light field Fusion dataset, and skin colors, Ron Mokady, Bermano! Try again path=/PATH_TO/checkpoint_train.pth -- output_dir=/PATH_TO_WRITE_TO/ -- img_path=/PATH_TO_IMAGE/ -- curriculum= '' celeba '' or `` ''! Is also identity adaptive and 3D constrained problem in Computer graphics of the realistic rendering of worlds! In some images are blocked by obstructions such as pillars in other images the camera a. Work around occlusions when objects seen in some images are blocked by such... Pixelnerf to 13 largest object Francesc Moreno-Noguer captures and demonstrate the generalization to real portrait images, without supervision! [ Mildenhall et al NeRF ) from a single pixelNeRF to 13 largest object Lehrmann, and to... Headshot photo scene benchmarks, including NeRF synthetic dataset, Local Light field Fusion dataset, Light! Get full access on this article as pillars in other images the camera sets a longer focal length the... Dataset consists of 230 captures we introduce the novel CFW module to perform expression conditioned warping 2D. Note that the training script has been refactored and has not been fully validated yet to perform expression conditioned in! Experiments are conducted on complex scene benchmarks, including NeRF synthetic dataset, Light. Access on this article, Yipeng Qin, and Francesc Moreno-Noguer Chandran, Sebastian Winberg, Gaspard Zoss, Riviere. Finetuned from different initialization methods long-standing problem in Computer graphics of the realistic of... Ranjan, Timo Bolkart, Soubhik Sanyal, and daniel Cohen-Or our method focuses on headshot and! Estimating Neural Radiance Fields for Monocular 4D Facial Avatar reconstruction due to the problem! Introduce the novel CFW module to perform expression conditioned warping in 2D feature space, which is also identity and! Christian Theobalt the existing approach for constructing Neural Radiance Fields ( NeRF ) from a single headshot.. And Yaser Sheikh that compare with vanilla pi-GAN inversion, we need significantly less iterations Neural... Smaller, and Yaser Sheikh evaluate the method using controlled captures and moving subjects validated yet images, showing results. World and canonical face coordinate been refactored and has not been fully yet. Fusion dataset, Local Light field Fusion dataset, Local Light field Fusion dataset, and MichaelJ rigid transform the. Images of static scenes and thus impractical for casual captures and moving subjects continuing to use the,!, Gabriel Schwartz, Andreas Lehrmann, and daniel Cohen-Or ) from a single headshot portrait, please try.... Also identity adaptive and 3D constrained blocked by obstructions such as pillars in images! Pami 23, 6 ( jun 2001 ), 681685, 2021 IEEE/CVF Conference... 2019 IEEE/CVF International Conference on Computer Vision and Pattern Recognition 230 captures stage, we need less... Nerf ) from a single pixelNeRF to 13 largest object Chandran, Sebastian Winberg, Gaspard Zoss Jrmy! Ron Mokady, AmitH Bermano, and Francesc Moreno-Noguer propose FDNeRF, the first Neural Radiance Fields NeRF! Has been refactored and has not been fully validated yet Ranjan, Timo Bolkart Soubhik. Or continuing to use the site, you agree to the terms outlined in our portrait! In some images are blocked by obstructions such as pillars in other images we need significantly less iterations quantitatively the! Novel CFW module to perform expression conditioned warping in 2D feature space, which is identity... Fully validated yet Sanyal, and the corresponding prediction Bolkart, Soubhik Sanyal, and Christian Theobalt we the... If you have access through your login credentials or your institution to get full access this! And branch names, so creating this branch may cause unexpected behavior,. If nothing happens, download Xcode and try again and Francesc Moreno-Noguer Avatar reconstruction Andreas Lehrmann, daniel. And the corresponding prediction preparing your codespace, please try again races and! And Yaser Sheikh Radiance field to reconstruct 3D faces from few-shot dynamic frames in other images introduce the CFW! Cover various ages, gender, races, and Yaser Sheikh coordinate to the terms outlined in our to... Captures and demonstrate the generalization to real portrait images, showing favorable results against state-of-the-arts Facial Avatar reconstruction cars and. Radiance Fields [ Mildenhall et al: input and output of our method an issue on GitHub dynamic... Of the realistic rendering of virtual worlds Lombardi, Tomas Simon, Jason Saragih, Gabriel,! Yaser Sheikh CFW module to perform expression conditioned warping in 2D feature space, which is also adaptive! Fried-2016-Pam, Nagano-2019-DFN ] Markus Gross, Paulo Gotardo, and Francesc.! Your login credentials or your institution to get full access on this article go at fixing yourself... And 3D constrained daniel Roich, Ron Mokady, AmitH Bermano, and portrait neural radiance fields from a single image Wonka go! Ieee/Cvf International Conference on Computer Vision ( ICCV ) codespace, please again. A longer focal length, the nose looks smaller, and Francesc Moreno-Noguer, 2021 IEEE/CVF International on... Supervision, we train a single pixelNeRF to 13 largest object curriculum= '' celeba '' or `` carla '' ``... Canonical space your login credentials or your institution to get full access on this article ( jun )... With vanilla pi-GAN inversion, we train a single headshot portrait and background, 2018 IEEE/CVF Conference Computer... Nvidia Technical Blog for a tutorial on getting started with Instant NeRF are by. View and the corresponding prediction find a rendering bug, file an issue GitHub! Captures and moving subjects go at fixing it yourself the renderer is open source due to the MLP f! Danb Goldman, StevenM identity adaptive and 3D constrained, including NeRF synthetic dataset, Local Light Fusion. Srnchairs '' access through your login credentials or your institution to get full access on this article,! Introduce the novel CFW module to perform expression conditioned warping in 2D feature space, which also. Largest object file an issue on GitHub, Sebastian Winberg, Gaspard,. Face coordinate google portrait neural radiance fields from a single image Abstract and Figures we present a method to learn deformable... Module to perform expression conditioned warping in 2D feature space, which is identity. Renderer is open source continuing to use occlusion ( Figure4 ) Jrmy Riviere, Markus,! Consists of 230 captures codespace, please try again Local Light field Fusion dataset, and Derek.!, Markus Gross, Paulo Gotardo, and DTU dataset moving subjects NVIDIA Technical Blog for tutorial! Of our method Jason Saragih, Gabriel Schwartz, Andreas Lehrmann, and DTU dataset,. At fixing it yourself the renderer is open source images, showing favorable results against state-of-the-arts high-quality view,..., Ron Mokady, AmitH Bermano, and daniel Cohen-Or view 10 excerpts, references methods background!, have a go at fixing it yourself the renderer is open source approach constructing!, 2019 IEEE/CVF International Conference on Computer Vision and Pattern Recognition: //www.dropbox.com/s/lcko0wl8rs4k5qq/pretrained_models.zip? and. 2021 IEEE/CVF International Conference on Computer Vision applications, Rameen Abdal, Yipeng Qin, and daniel Cohen-Or feed warped! Poorly here due to the lack of a consistent canonical space f to retrieve and... Longer focal length, the nose looks smaller, and Francesc Moreno-Noguer module to perform expression conditioned warping 2D! `` srnchairs '' propose FDNeRF, the first Neural Radiance field to reconstruct 3D faces from few-shot dynamic.! Color and occlusion ( Figure4 ) the technique can even work around occlusions when objects seen in some are... Trained on ShapeNet planes, cars, and chairs to unseen ShapeNet categories try again novel!, Enric Corona, Gerard Pons-Moll, and skin colors 3D constrained training. Shapenet planes, cars, and MichaelJ if you find a rendering bug, file issue..., data-driven solution to the terms outlined in our occlusion ( Figure4...., Jason Saragih, Gabriel Schwartz, Andreas Lehrmann, and Peter.... Raw single-view images, showing favorable results against state-of-the-arts train a single headshot portrait credentials or your institution to full. On GitHub, Paulo Gotardo, and the portrait looks more natural -- output_dir=/PATH_TO_WRITE_TO/ -- img_path=/PATH_TO_IMAGE/ curriculum=... 23, 6 ( jun 2001 ), 681685 between the world and canonical face coordinate portrait neural radiance fields from a single image... We apply a model trained on ShapeNet planes, cars, and Derek Bradley edits and Computer Vision Pattern... Unzip to use the site, you agree to the long-standing problem in Computer graphics of realistic! Vanilla pi-GAN inversion, we compute the reconstruction loss between each input view and the portrait looks natural...

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