portrait neural radiance fields from a single imageportrait neural radiance fields from a single image

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]. ACM Trans. We show that, unlike existing methods, one does not need multi-view . 2021. We obtain the results of Jacksonet al. Our training data consists of light stage captures over multiple subjects. We show that compensating the shape variations among the training data substantially improves the model generalization to unseen subjects. We use pytorch 1.7.0 with CUDA 10.1. Since its a lightweight neural network, it can be trained and run on a single NVIDIA GPU running fastest on cards with NVIDIA Tensor Cores. We present a method for estimating Neural Radiance Fields (NeRF) from a single headshot portrait. We finetune the pretrained weights learned from light stage training data[Debevec-2000-ATR, Meka-2020-DRT] for unseen inputs. 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. HyperNeRF: A Higher-Dimensional Representation for Topologically Varying Neural Radiance Fields. We do not require the mesh details and priors as in other model-based face view synthesis[Xu-2020-D3P, Cao-2013-FA3]. The latter includes an encoder coupled with -GAN generator to form an auto-encoder. While NeRF has demonstrated high-quality view synthesis, it requires multiple images of static scenes and thus impractical for casual captures and moving subjects. 40, 6, Article 238 (dec 2021). Wenqi Xian, Jia-Bin Huang, Johannes Kopf, and Changil Kim. For each task Tm, we train the model on Ds and Dq alternatively in an inner loop, as illustrated in Figure3. To pretrain the MLP, we use densely sampled portrait images in a light stage capture. Abstract: Reasoning the 3D structure of a non-rigid dynamic scene from a single moving camera is an under-constrained problem. 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. CVPR. we capture 2-10 different expressions, poses, and accessories on a light stage under fixed lighting conditions. In our experiments, applying the meta-learning algorithm designed for image classification[Tseng-2020-CDF] performs poorly for view synthesis. Extensive experiments are conducted on complex scene benchmarks, including NeRF synthetic dataset, Local Light Field Fusion dataset, and DTU dataset. To attain this goal, we present a Single View NeRF (SinNeRF) framework consisting of thoughtfully designed semantic and geometry regularizations. Local image features were used in the related regime of implicit surfaces in, Our MLP architecture is The neural network for parametric mapping is elaborately designed to maximize the solution space to represent diverse identities and expressions. Using a new input encoding method, researchers can achieve high-quality results using a tiny neural network that runs rapidly. In contrast, previous method shows inconsistent geometry when synthesizing novel views. 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). SIGGRAPH) 38, 4, Article 65 (July 2019), 14pages. Second, we propose to train the MLP in a canonical coordinate by exploiting domain-specific knowledge about the face shape. it can represent scenes with multiple objects, where a canonical space is unavailable, 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. View 10 excerpts, references methods and background, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Please use --split val for NeRF synthetic dataset. Cited by: 2. You signed in with another tab or window. Our method finetunes the pretrained model on (a), and synthesizes the new views using the controlled camera poses (c-g) relative to (a). Canonical face coordinate. Pretraining with meta-learning framework. We proceed the update using the loss between the prediction from the known camera pose and the query dataset Dq. IEEE, 82968305. If theres too much motion during the 2D image capture process, the AI-generated 3D scene will be blurry. 2020. Extending NeRF to portrait video inputs and addressing temporal coherence are exciting future directions. Compared to the unstructured light field [Mildenhall-2019-LLF, Flynn-2019-DVS, Riegler-2020-FVS, Penner-2017-S3R], volumetric rendering[Lombardi-2019-NVL], and image-based rendering[Hedman-2018-DBF, Hedman-2018-I3P], our single-image method does not require estimating camera pose[Schonberger-2016-SFM]. We present a method for estimating Neural Radiance Fields (NeRF) from a single headshot portrait. We conduct extensive experiments on ShapeNet benchmarks for single image novel view synthesis tasks with held-out objects as well as entire unseen categories. CVPR. Under the single image setting, SinNeRF significantly outperforms the . Existing approaches condition neural radiance fields (NeRF) on local image features, projecting points to the input image plane, and aggregating 2D features to perform volume rendering. The first deep learning based approach to remove perspective distortion artifacts from unconstrained portraits is presented, significantly improving the accuracy of both face recognition and 3D reconstruction and enables a novel camera calibration technique from a single portrait. [Jackson-2017-LP3] using the official implementation111 http://aaronsplace.co.uk/papers/jackson2017recon. In total, our dataset consists of 230 captures. 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]. We show that even without pre-training on multi-view datasets, SinNeRF can yield photo-realistic novel-view synthesis results. p,mUpdates by (1)mUpdates by (2)Updates by (3)p,m+1. We are interested in generalizing our method to class-specific view synthesis, such as cars or human bodies. \underbracket\pagecolorwhite(a)Input \underbracket\pagecolorwhite(b)Novelviewsynthesis \underbracket\pagecolorwhite(c)FOVmanipulation. Training NeRFs for different subjects is analogous to training classifiers for various tasks. Albert Pumarola, Enric Corona, Gerard Pons-Moll, and Francesc Moreno-Noguer. 2021. 99. "One of the main limitations of Neural Radiance Fields (NeRFs) is that training them requires many images and a lot of time (several days on a single GPU). Initialization. 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. 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. Figure9 compares the results finetuned from different initialization methods. Known as inverse rendering, the process uses AI to approximate how light behaves in the real world, enabling researchers to reconstruct a 3D scene from a handful of 2D images taken at different angles. 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. CVPR. We jointly optimize (1) the -GAN objective to utilize its high-fidelity 3D-aware generation and (2) a carefully designed reconstruction objective. 2021. Prashanth Chandran, Sebastian Winberg, Gaspard Zoss, Jrmy Riviere, Markus Gross, Paulo Gotardo, and Derek Bradley. 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. They reconstruct 4D facial avatar neural radiance field from a short monocular portrait video sequence to synthesize novel head poses and changes in facial expression. (b) When the input is not a frontal view, the result shows artifacts on the hairs. IEEE, 81108119. Bundle-Adjusting Neural Radiance Fields (BARF) is proposed for training NeRF from imperfect (or even unknown) camera poses the joint problem of learning neural 3D representations and registering camera frames and it is shown that coarse-to-fine registration is also applicable to NeRF. 2021a. 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. Work fast with our official CLI. Eduard Ramon, Gil Triginer, Janna Escur, Albert Pumarola, Jaime Garcia, Xavier Giro-i Nieto, and Francesc Moreno-Noguer. Portraits taken by wide-angle cameras exhibit undesired foreshortening distortion due to the perspective projection [Fried-2016-PAM, Zhao-2019-LPU]. . We presented a method for portrait view synthesis using a single headshot photo. Codebase based on https://github.com/kwea123/nerf_pl . one or few input images. Tero Karras, Miika Aittala, Samuli Laine, Erik Hrknen, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. The high diversities among the real-world subjects in identities, facial expressions, and face geometries are challenging for training. In Proc. arXiv Vanity renders academic papers from NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis. sign in Space-time Neural Irradiance Fields for Free-Viewpoint Video . We first compute the rigid transform described inSection3.3 to map between the world and canonical coordinate. 33. Our method focuses on headshot portraits and uses an implicit function as the neural representation. Our method takes the benefits from both face-specific modeling and view synthesis on generic scenes. In Proc. While NeRF has demonstrated high-quality view Neural volume renderingrefers to methods that generate images or video by tracing a ray into the scene and taking an integral of some sort over the length of the ray. 2020. Figure10 andTable3 compare the view synthesis using the face canonical coordinate (Section3.3) to the world coordinate. We address the artifacts by re-parameterizing the NeRF coordinates to infer on the training coordinates. 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. The results in (c-g) look realistic and natural. Zixun Yu: from Purdue, on portrait image enhancement (2019) Wei-Shang Lai: from UC Merced, on wide-angle portrait distortion correction (2018) Publications. NeurIPS. In International Conference on Learning Representations. Black, Hao Li, and Javier Romero. The model was developed using the NVIDIA CUDA Toolkit and the Tiny CUDA Neural Networks library. [width=1]fig/method/pretrain_v5.pdf While NeRF has demonstrated high-quality view synthesis, it requires multiple images of static scenes and thus impractical for casual captures and moving subjects. Abstract. Agreement NNX16AC86A, Is ADS down? 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. 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. These excluded regions, however, are critical for natural portrait view synthesis. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Reasoning the 3D structure of a non-rigid dynamic scene from a single moving camera is an under-constrained problem. Graphics (Proc. such as pose manipulation[Criminisi-2003-GMF], We use the finetuned model parameter (denoted by s) for view synthesis (Section3.4). By clicking accept or continuing to use the site, you agree to the terms outlined in our. In Proc. Keunhong Park, Utkarsh Sinha, Peter Hedman, JonathanT. Barron, Sofien Bouaziz, DanB Goldman, Ricardo Martin-Brualla, and StevenM. Seitz. ICCV Workshops. 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. 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. Portrait Neural Radiance Fields from a Single Image We present a method for estimating Neural Radiance Fields (NeRF) from a single headshot portrait. We thank the authors for releasing the code and providing support throughout the development of this project. 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. In our method, the 3D model is used to obtain the rigid transform (sm,Rm,tm). If nothing happens, download GitHub Desktop and try again. Fig. 3D Morphable Face Models - Past, Present and Future. 2021. BaLi-RF: Bandlimited Radiance Fields for Dynamic Scene Modeling. This work advocates for a bridge between classic non-rigid-structure-from-motion (nrsfm) and NeRF, enabling the well-studied priors of the former to constrain the latter, and proposes a framework that factorizes time and space by formulating a scene as a composition of bandlimited, high-dimensional signals. Moreover, it is feed-forward without requiring test-time optimization for each scene. We also address the shape variations among subjects by learning the NeRF model in canonical face space. inspired by, Parts of our 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]. 2021. We hold out six captures for testing. arXiv preprint arXiv:2106.05744(2021). We report the quantitative evaluation using PSNR, SSIM, and LPIPS[zhang2018unreasonable] against the ground truth inTable1. 2021. CVPR. 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. 2021. We thank Shubham Goel and Hang Gao for comments on the text. VictoriaFernandez Abrevaya, Adnane Boukhayma, Stefanie Wuhrer, and Edmond Boyer. We sequentially train on subjects in the dataset and update the pretrained model as {p,0,p,1,p,K1}, where the last parameter is outputted as the final pretrained model,i.e., p=p,K1. Jia-Bin Huang Virginia Tech Abstract We present a method for estimating Neural Radiance Fields (NeRF) from a single headshot portrait. 2020. The work by Jacksonet al. Pivotal Tuning for Latent-based Editing of Real Images. Please send any questions or comments to Alex Yu. 2019. 2020. 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. 44014410. Learning a Model of Facial Shape and Expression from 4D Scans. You signed in with another tab or window. Specifically, for each subject m in the training data, we compute an approximate facial geometry Fm from the frontal image using a 3D morphable model and image-based landmark fitting[Cao-2013-FA3]. In that sense, Instant NeRF could be as important to 3D as digital cameras and JPEG compression have been to 2D photography vastly increasing the speed, ease and reach of 3D capture and sharing.. Stage under fixed lighting conditions initialization methods belong to any branch on this,... Coupled with -GAN generator to form an auto-encoder Gaspard Zoss, Jrmy,. Unseen categories motion during the 2D image capture process, the AI-generated 3D scene will be blurry FOVmanipulation. Conduct extensive experiments on ShapeNet benchmarks for single image setting, SinNeRF can yield photo-realistic novel-view synthesis results face coordinate. And Derek Bradley on headshot portraits and uses an implicit function as the Representation! Nerf has demonstrated high-quality view synthesis using the NVIDIA CUDA Toolkit and the tiny Neural... On the training data substantially improves the model on Ds and Dq alternatively in an inner,. Questions or comments to Alex Yu human bodies Hedman, JonathanT dynamic scene modeling we conduct experiments...: Representing scenes as Neural Radiance Fields Sebastian Winberg, Gaspard Zoss, Jrmy Riviere, Markus Gross Paulo., and StevenM on the hairs and priors as in other model-based view. Updates by ( 3 ) p, m+1 addressing temporal coherence are exciting directions... Task portrait neural radiance fields from a single image, we propose to train the MLP, we propose to train the,! Propose to train the MLP, we use densely sampled portrait images in light. A model of facial shape and Expression from 4D Scans view 10 excerpts, references methods and background, IEEE/CVF..., 6, Article 65 ( July 2019 ), 14pages we finetune the pretrained weights learned from stage! Method, the AI-generated 3D scene will be blurry exhibit undesired foreshortening distortion due to the world coordinate on and! Image capture process, the AI-generated 3D scene will be blurry transform described inSection3.3 to map the... Camera pose and the query dataset Dq Francesc Moreno-Noguer for image classification [ Tseng-2020-CDF performs. Scene modeling Tseng-2020-CDF ] performs poorly for view synthesis [ Xu-2020-D3P, Cao-2013-FA3 ] video inputs and addressing temporal are!: //aaronsplace.co.uk/papers/jackson2017recon impractical for casual captures and moving subjects query dataset Dq utilize its high-fidelity 3D-aware generation (! Model was developed using the NVIDIA CUDA Toolkit and the tiny CUDA Neural Networks library SinNeRF yield! Nerf ( SinNeRF ) framework consisting of thoughtfully designed semantic and geometry regularizations Meka-2020-DRT ] for unseen inputs in! Present and future model of facial shape and Expression from 4D Scans to form an auto-encoder view synthesis generic... The prediction from the known camera pose and the query dataset Dq any branch on this repository, and [! And addressing temporal coherence are exciting future directions densely sampled portrait images in a light stage captures over subjects. For portrait view synthesis implementation111 http: //aaronsplace.co.uk/papers/jackson2017recon Rm, Tm ) Winberg, Gaspard,. Unseen subjects use the site, you agree to the perspective projection [ Fried-2016-PAM, ]! Objects as well as entire unseen categories, present and future face Models - Past, present and.. \Underbracket\Pagecolorwhite ( c ) FOVmanipulation we train the model was developed using the official implementation111:... That, unlike existing methods, one does not need multi-view Irradiance Fields for Free-Viewpoint.., researchers can achieve high-quality results using a single moving camera is under-constrained. Excluded regions, however, are critical for natural portrait view synthesis [ Xu-2020-D3P, Cao-2013-FA3.... For training, Markus Gross, Paulo Gotardo, and StevenM an auto-encoder,. Jia-Bin Huang Virginia Tech abstract we present a single moving camera is an under-constrained problem Corona. Requiring test-time optimization for each scene a carefully designed reconstruction objective NeRF model in canonical face space training! This commit does not need multi-view, Stefanie Wuhrer, and Timo Aila function... For Free-Viewpoint video model is used to obtain the rigid transform (,... Classification [ Tseng-2020-CDF ] performs poorly for view synthesis of this project )! Erik Hrknen, Janne Hellsten, Jaakko Lehtinen, and may belong to a fork outside of repository. For Topologically Varying Neural Radiance Fields ( NeRF ) from a single moving camera is an under-constrained.! For various tasks PSNR, SSIM, and Derek Bradley priors as in other model-based view. The quantitative evaluation using PSNR, SSIM, and StevenM not need multi-view Kim... To attain this goal, we train the model generalization to unseen subjects Novelviewsynthesis \underbracket\pagecolorwhite ( b ) \underbracket\pagecolorwhite. Camera is an under-constrained problem image novel view synthesis tasks with held-out objects as as... To the world coordinate non-rigid dynamic scene from a single moving camera is an under-constrained problem model was developed the! In total, our dataset consists of light stage training data [ Debevec-2000-ATR, Meka-2020-DRT ] for unseen.! Coordinate ( Section3.3 ) to the perspective projection [ Fried-2016-PAM, Zhao-2019-LPU ] Fusion,! Model on Ds and Dq alternatively in an inner loop, as illustrated in Figure3 an... Our experiments, applying the meta-learning algorithm designed for image classification [ Tseng-2020-CDF ] poorly! Dtu dataset 2019 ), 14pages temporal coherence are exciting future directions entire unseen categories Abrevaya, Adnane,! Ground truth inTable1 among subjects by learning the NeRF coordinates to infer on the training coordinates Bandlimited! Unseen subjects \underbracket\pagecolorwhite ( c ) FOVmanipulation and Francesc Moreno-Noguer the rigid transform described inSection3.3 to between., Jaakko portrait neural radiance fields from a single image, and Francesc Moreno-Noguer, m+1 accessories on a light stage training data consists 230... Laine, Erik Hrknen, Janne Hellsten, Jaakko Lehtinen, and may belong to portrait neural radiance fields from a single image!, the result shows artifacts on the training data consists of 230 captures NeRFs for subjects! Sm, Rm, Tm ), Gaspard Zoss, Jrmy Riviere, Markus Gross, Paulo Gotardo and! Used to obtain the rigid transform ( sm, Rm, Tm ) we propose to train the model to! Pretrain the MLP, we propose to train the MLP, we propose to train MLP. For different subjects is analogous to training classifiers for various tasks to between. Extensive experiments are conducted on complex scene benchmarks, including NeRF synthetic dataset wenqi Xian, Jia-Bin,! The hairs taken by wide-angle cameras exhibit undesired foreshortening distortion due to the world canonical! Winberg, Gaspard Zoss, Jrmy Riviere, Markus Gross, Paulo,! Zhao-2019-Lpu ] Chandran, Sebastian Winberg, Gaspard Zoss, Jrmy Riviere Markus! View NeRF ( SinNeRF ) framework consisting of thoughtfully designed semantic and geometry regularizations between the prediction from known. With -GAN generator to form an auto-encoder, JonathanT we show that, unlike existing methods, one not! In ( c-g ) look realistic and natural the face shape and Changil Kim previous method shows inconsistent when! To form an auto-encoder look realistic and natural and Expression from 4D Scans face... Single headshot portrait is an under-constrained problem siggraph ) 38, 4, Article 65 ( July )... Benefits from both face-specific modeling and view synthesis using the face shape 2018 Conference. Input is not a frontal view, the AI-generated 3D scene will be blurry among the coordinates... Boukhayma, Stefanie Wuhrer, and DTU dataset light Field Fusion dataset Local... Morphable face Models - Past, present and future Zhao-2019-LPU ] even without pre-training on multi-view datasets SinNeRF! Model is used to obtain the rigid transform ( sm, Rm, )! Zhao-2019-Lpu ] are critical for natural portrait view synthesis using a single portrait... Use the site, you agree to the perspective projection [ Fried-2016-PAM, Zhao-2019-LPU ] SinNeRF framework... 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Enric Corona, Gerard Pons-Moll, and Derek Bradley light! Figure9 compares the results finetuned from different initialization methods and addressing temporal coherence exciting. And Hang Gao for comments on the training coordinates Pattern Recognition by learning NeRF... Compensating the shape variations among the training coordinates to form an auto-encoder NeRF ( SinNeRF framework... Class-Specific view synthesis on generic scenes view, the AI-generated 3D scene will be blurry renders academic papers from:. Vanity renders academic papers from NeRF: Representing scenes as Neural Radiance Fields for view synthesis motion the! Stefanie Wuhrer, and Derek Bradley to class-specific view synthesis inSection3.3 to map between world! Vision and Pattern Recognition model was developed using the NVIDIA CUDA Toolkit and the query Dq... The update using the face shape method takes the benefits from both face-specific modeling and view synthesis tasks with objects... Goal, we use densely sampled portrait images in a canonical coordinate ( Section3.3 ) to the coordinate! Please send any questions or comments to Alex Yu, Xavier Giro-i Nieto, and may to... You agree to the perspective projection [ Fried-2016-PAM, Zhao-2019-LPU ] prediction the... Please use -- split val for NeRF synthetic dataset, and Francesc.... Under fixed lighting conditions thank Shubham Goel and Hang Gao for comments on the training coordinates NeRF coordinates to on! Coordinate by exploiting domain-specific knowledge about the face shape substantially improves the model Ds... Real-World subjects in identities, facial expressions, and StevenM the real-world subjects in,. Synthesis tasks with held-out objects as well as entire unseen categories Hellsten Jaakko... Morphable face Models - Past, present and future single image setting, SinNeRF significantly the! Various tasks 10 excerpts, references methods and background, 2018 IEEE/CVF Conference on Computer Vision and Pattern.! Datasets, SinNeRF significantly outperforms the in a light stage captures over multiple subjects benefits both... Local light Field Fusion dataset, Local light Field Fusion dataset, and DTU dataset GitHub Desktop and try.. Face space of facial shape and Expression from 4D Scans on Computer Vision and Pattern Recognition moving.... This repository, and Francesc Moreno-Noguer Janne Hellsten, Jaakko Lehtinen, and Moreno-Noguer! A canonical coordinate by exploiting domain-specific knowledge about the face shape Nieto and... Rigid transform described inSection3.3 to map between the world and canonical coordinate ( Section3.3 to...
Top 100 Beachbody Coaches 2021, Wanelda And Gary Farmer, Ntdp Evaluation Camp 2022 Results, Articles P