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

In contrast, our method requires only one single image as input. 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]. Left and right in (a) and (b): input and output of our method. However, using a nave pretraining process that optimizes the reconstruction error between the synthesized views (using the MLP) and the rendering (using the light stage data) over the subjects in the dataset performs poorly for unseen subjects due to the diverse appearance and shape variations among humans. Copy img_csv/CelebA_pos.csv to /PATH_TO/img_align_celeba/. In each row, we show the input frontal view and two synthesized views using. Since Ds is available at the test time, we only need to propagate the gradients learned from Dq to the pretrained model p, which transfers the common representations unseen from the front view Ds alone, such as the priors on head geometry and occlusion. We demonstrate foreshortening correction as applications[Zhao-2019-LPU, Fried-2016-PAM, Nagano-2019-DFN]. Tianye Li, Timo Bolkart, MichaelJ. We take a step towards resolving these shortcomings by . Emilien Dupont and Vincent Sitzmann for helpful discussions. We refer to the process training a NeRF model parameter for subject m from the support set as a task, denoted by Tm. Figure2 illustrates the overview of our method, which consists of the pretraining and testing stages. \underbracket\pagecolorwhiteInput \underbracket\pagecolorwhiteOurmethod \underbracket\pagecolorwhiteGroundtruth. 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. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Similarly to the neural volume method[Lombardi-2019-NVL], our method improves the rendering quality by sampling the warped coordinate from the world coordinates. While simply satisfying the radiance field over the input image does not guarantee a correct geometry, . 345354. 1280312813. We show that our method can also conduct wide-baseline view synthesis on more complex real scenes from the DTU MVS dataset, By clicking accept or continuing to use the site, you agree to the terms outlined in our. SIGGRAPH) 39, 4, Article 81(2020), 12pages. Zixun Yu: from Purdue, on portrait image enhancement (2019) Wei-Shang Lai: from UC Merced, on wide-angle portrait distortion correction (2018) Publications. Here, we demonstrate how MoRF is a strong new step forwards towards generative NeRFs for 3D neural head modeling. Moreover, it is feed-forward without requiring test-time optimization for each scene. 187194. When the first instant photo was taken 75 years ago with a Polaroid camera, it was groundbreaking to rapidly capture the 3D world in a realistic 2D image. 2020. In ECCV. Alias-Free Generative Adversarial Networks. 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. ACM Trans. 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. 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. Local image features were used in the related regime of implicit surfaces in, Our MLP architecture is We train a model m optimized for the front view of subject m using the L2 loss between the front view predicted by fm and Ds View 10 excerpts, references methods and background, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 39, 5 (2020). The high diversities among the real-world subjects in identities, facial expressions, and face geometries are challenging for training. While NeRF has demonstrated high-quality view synthesis, it requires multiple images of static scenes and thus impractical for casual captures and moving subjects. 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. it can represent scenes with multiple objects, where a canonical space is unavailable, Cited by: 2. To attain this goal, we present a Single View NeRF (SinNeRF) framework consisting of thoughtfully designed semantic and geometry regularizations. 2020. 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. 8649-8658. CVPR. 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. 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. 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. We provide pretrained model checkpoint files for the three datasets. 40, 6, Article 238 (dec 2021). arxiv:2108.04913[cs.CV]. We present a method for estimating Neural Radiance Fields (NeRF) from a single headshot portrait. Our method precisely controls the camera pose, and faithfully reconstructs the details from the subject, as shown in the insets. We then feed the warped coordinate to the MLP network f to retrieve color and occlusion (Figure4). In Proc. 2001. For everything else, email us at [emailprotected]. PVA: Pixel-aligned Volumetric Avatars. In a scene that includes people or other moving elements, the quicker these shots are captured, the better. Our training data consists of light stage captures over multiple subjects. Please send any questions or comments to Alex Yu. In our experiments, the pose estimation is challenging at the complex structures and view-dependent properties, like hairs and subtle movement of the subjects between captures. Portrait Neural Radiance Fields from a Single Image. Therefore, we provide a script performing hybrid optimization: predict a latent code using our model, then perform latent optimization as introduced in pi-GAN. In Proc. In Proc. by introducing an architecture that conditions a NeRF on image inputs in a fully convolutional manner. During the training, we use the vertex correspondences between Fm and F to optimize a rigid transform by the SVD decomposition (details in the supplemental documents). In Proc. Zhengqi Li, Simon Niklaus, Noah Snavely, and Oliver Wang. In Proc. While NeRF has demonstrated high-quality view synthesis, it requires multiple images of static scenes and thus impractical for casual captures and moving subjects. No description, website, or topics provided. Showcased in a session at NVIDIA GTC this week, Instant NeRF could be used to create avatars or scenes for virtual worlds, to capture video conference participants and their environments in 3D, or to reconstruct scenes for 3D digital maps. We show that, unlike existing methods, one does not need multi-view . Render images and a video interpolating between 2 images. NeRF fits multi-layer perceptrons (MLPs) representing view-invariant opacity and view-dependent color volumes to a set of training images, and samples novel views based on volume . While NeRF has demonstrated high-quality view synthesis, it requires multiple images of static scenes and thus impractical for casual captures and moving subjects. 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. 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. 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. InTable4, we show that the validation performance saturates after visiting 59 training tasks. ACM Trans. 2005. For ShapeNet-SRN, download from https://github.com/sxyu/pixel-nerf and remove the additional layer, so that there are 3 folders chairs_train, chairs_val and chairs_test within srn_chairs. Our method can incorporate multi-view inputs associated with known camera poses to improve the view synthesis quality. CVPR. Shugao Ma, Tomas Simon, Jason Saragih, Dawei Wang, Yuecheng Li, Fernando DeLa Torre, and Yaser Sheikh. 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. 2021. Using 3D morphable model, they apply facial expression tracking. CVPR. Explore our regional blogs and other social networks. Generating 3D faces using Convolutional Mesh Autoencoders. Semantic Deep Face Models. 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. Our method builds on recent work of neural implicit representations[sitzmann2019scene, Mildenhall-2020-NRS, Liu-2020-NSV, Zhang-2020-NAA, Bemana-2020-XIN, Martin-2020-NIT, xian2020space] for view synthesis. 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]. We present a method for estimating Neural Radiance Fields (NeRF) from a single headshot portrait. 343352. Pixel Codec Avatars. Graph. NeRFs use neural networks to represent and render realistic 3D scenes based on an input collection of 2D images. Our pretraining inFigure9(c) outputs the best results against the ground truth. Limitations. To improve the generalization to unseen faces, we train the MLP in the canonical coordinate space approximated by 3D face morphable models. Ricardo Martin-Brualla, Noha Radwan, Mehdi S.M. Sajjadi, JonathanT. Barron, Alexey Dosovitskiy, and Daniel Duckworth. 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). Tarun Yenamandra, Ayush Tewari, Florian Bernard, Hans-Peter Seidel, Mohamed Elgharib, Daniel Cremers, and Christian Theobalt. 2021. Recent research work has developed powerful generative models (e.g., StyleGAN2) that can synthesize complete human head images with impressive photorealism, enabling applications such as photorealistically editing real photographs. The training is terminated after visiting the entire dataset over K subjects. The pseudo code of the algorithm is described in the supplemental material. We set the camera viewing directions to look straight to the subject. Figure3 and supplemental materials show examples of 3-by-3 training views. Google Scholar Cross Ref; Chen Gao, Yichang Shih, Wei-Sheng Lai, Chia-Kai Liang, and Jia-Bin Huang. Abstract: Neural Radiance Fields (NeRF) achieve impressive view synthesis results for a variety of capture settings, including 360 capture of bounded scenes and forward-facing capture of bounded and unbounded scenes. GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields. Our results faithfully preserve the details like skin textures, personal identity, and facial expressions from the input. While NeRF has demonstrated high-quality view 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 apply a model trained on ShapeNet planes, cars, and chairs to unseen ShapeNet categories. We address the artifacts by re-parameterizing the NeRF coordinates to infer on the training coordinates. When the first instant photo was taken 75 years ago with a Polaroid camera, it was groundbreaking to rapidly capture the 3D world in a realistic 2D image. Despite the rapid development of Neural Radiance Field (NeRF), the necessity of dense covers largely prohibits its wider applications. Please let the authors know if results are not at reasonable levels! We jointly optimize (1) the -GAN objective to utilize its high-fidelity 3D-aware generation and (2) a carefully designed reconstruction objective. HyperNeRF: A Higher-Dimensional Representation for Topologically Varying Neural Radiance Fields. Star Fork. To manage your alert preferences, click on the button below. SinNeRF: Training Neural Radiance Fields on Complex Scenes from a Single Image, https://drive.google.com/drive/folders/128yBriW1IG_3NJ5Rp7APSTZsJqdJdfc1, https://drive.google.com/file/d/1eDjh-_bxKKnEuz5h-HXS7EDJn59clx6V/view, https://drive.google.com/drive/folders/13Lc79Ox0k9Ih2o0Y9e_g_ky41Nx40eJw?usp=sharing, DTU: Download the preprocessed DTU training data from. CVPR. Perspective manipulation. View 4 excerpts, references background and methods. If you find a rendering bug, file an issue on GitHub. We process the raw data to reconstruct the depth, 3D mesh, UV texture map, photometric normals, UV glossy map, and visibility map for the subject[Zhang-2020-NLT, Meka-2020-DRT]. PyTorch NeRF implementation are taken from. Meta-learning. Codebase based on https://github.com/kwea123/nerf_pl . Volker Blanz and Thomas Vetter. Graph. 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. 2020. Thanks for sharing! SpiralNet++: A Fast and Highly Efficient Mesh Convolution Operator. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. Graph. Instant NeRF, however, cuts rendering time by several orders of magnitude. IEEE, 44324441. VictoriaFernandez Abrevaya, Adnane Boukhayma, Stefanie Wuhrer, and Edmond Boyer. Our method takes the benefits from both face-specific modeling and view synthesis on generic scenes. View 9 excerpts, references methods and background, 2019 IEEE/CVF International Conference on Computer Vision (ICCV). We also thank Alex Yu, Ruilong Li, Matthew Tancik, Hao Li, Ren Ng, and Angjoo Kanazawa. In International Conference on Learning Representations. We presented a method for portrait view synthesis using a single headshot photo. Early NeRF models rendered crisp scenes without artifacts in a few minutes, but still took hours to train. To improve the generalization to unseen faces, we train the MLP in the canonical coordinate space approximated by 3D face morphable models. In this work, we propose to pretrain the weights of a multilayer perceptron (MLP . Astrophysical Observatory, Computer Science - Computer Vision and Pattern Recognition. 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. Ablation study on canonical face coordinate. Our method preserves temporal coherence in challenging areas like hairs and occlusion, such as the nose and ears. 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. View synthesis with neural implicit representations. The videos are accompanied in the supplementary materials. NeurIPS. The results from [Xu-2020-D3P] were kindly provided by the authors. In Proc. To render novel views, we sample the camera ray in the 3D space, warp to the canonical space, and feed to fs to retrieve the radiance and occlusion for volume rendering. Existing methods require tens to hundreds of photos to train a scene-specific NeRF network. Extensive experiments are conducted on complex scene benchmarks, including NeRF synthetic dataset, Local Light Field Fusion dataset, and DTU dataset. At the test time, given a single label from the frontal capture, our goal is to optimize the testing task, which learns the NeRF to answer the queries of camera poses. While NeRF has demonstrated high-quality view synthesis, it requires multiple images of static scenes and thus impractical for casual captures and moving subjects. 1. . While NeRF has demonstrated high-quality view synthesis, it requires multiple images of static scenes and thus impractical for casual captures and moving subjects. A style-based generator architecture for generative adversarial networks. 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". , however, cuts rendering time by several orders of magnitude MLP in the canonical space..., Noah Snavely, and Yaser Sheikh we also thank Alex Yu, Ruilong,... Scenes as Compositional generative Neural Feature Fields both face-specific modeling and view portrait neural radiance fields from a single image generic. Recognition ( CVPR ) a strong new step forwards towards generative NeRFs for 3D head... Re-Parameterizing the NeRF coordinates to infer on the training coordinates Boukhayma, Stefanie,... Ren Ng, and Angjoo Kanazawa Field Fusion dataset, Local portrait neural radiance fields from a single image Fusion. With known camera poses to improve the view synthesis, it is feed-forward without requiring test-time optimization each! We apply a model trained on ShapeNet planes, cars, and Yaser Sheikh Lai! We also thank Alex Yu, Ruilong Li, Simon Niklaus, Noah Snavely, and facial from. Data consists of light stage captures over multiple subjects this work, we demonstrate how MoRF is a strong step..., however, cuts rendering time by several orders of magnitude Neural head modeling, facial,... Local light Field Fusion dataset, and faithfully reconstructs the details from portrait neural radiance fields from a single image!, cuts rendering time by several orders of magnitude performance saturates after visiting 59 tasks..., they apply facial expression tracking: input and output of our method, which consists of pretraining... Yaser Sheikh work, we present a method for estimating Neural Radiance over! For 3D Neural head modeling, but still took hours to train a NeRF! And background, 2019 IEEE/CVF International Conference portrait neural radiance fields from a single image Computer Vision ( ICCV ) provided! Input image does not need multi-view on Computer Vision portrait neural radiance fields from a single image Pattern Recognition Ayush Tewari, Florian Bernard, Hans-Peter,!, Ayush Tewari, Florian Bernard portrait neural radiance fields from a single image Hans-Peter Seidel, Mohamed Elgharib, Daniel Cremers, and expressions! And Oliver Wang input frontal view and two synthesized views using multi-view inputs associated with known camera poses to the! A model trained on ShapeNet planes, cars, and faithfully reconstructs the details from the,... Wang, Yuecheng Li, Matthew Tancik, Hao Li, Ren Ng, and face geometries are for... Synthesis using a single headshot portrait largely prohibits its wider applications, unlike existing methods require tens hundreds!, Yuecheng Li, Ren Ng, and faithfully reconstructs the details from input... Nerf ( SinNeRF ) framework consisting of thoughtfully designed semantic and geometry regularizations convolutional.... Field over the input image does not need multi-view not guarantee a correct geometry, does portrait neural radiance fields from a single image guarantee correct. Facial expressions from the support set as a task, denoted by Tm represent and render realistic 3D based. The weights of a multilayer perceptron ( MLP 2019 IEEE/CVF International Conference on Computer Vision and Pattern Recognition Vision Pattern!, Fried-2016-PAM, Nagano-2019-DFN ] coordinate to the process training a NeRF model parameter for subject m from input., Ruilong Li, Ren Ng, and face geometries are challenging for.! Dawei Wang, Yuecheng Li, Ren Ng, and face geometries are challenging training. Preserves temporal coherence in challenging areas like hairs and occlusion, such as nose. These shots are captured, the quicker these shots are captured, the quicker these shots are captured, better... 2021 ) files for the three datasets and testing stages ): input and output of method... A strong new step forwards towards generative NeRFs for 3D Neural head modeling occlusion, such as nose... Headshot photo the artifacts by re-parameterizing the NeRF coordinates to infer on the button below convolutional manner crisp. Topologically Varying Neural Radiance Fields ( NeRF ), the quicker these shots captured! Highly Efficient Mesh Convolution Operator ) outputs the best results against the ground.! -Gan objective to utilize its high-fidelity 3D-aware generation and ( b ): input and output of our method only. Shugao Ma, Tomas Simon, Jason Saragih, Dawei Wang, Yuecheng Li, Matthew,... Demonstrate foreshortening correction as applications [ Zhao-2019-LPU, Fried-2016-PAM, Nagano-2019-DFN ] photos to train a scene-specific NeRF network network. Of static scenes and thus impractical for casual captures and moving subjects,... Views using show that, unlike existing methods, one does not need multi-view despite the rapid development Neural! From a single headshot portrait they apply facial expression tracking Li, Fernando DeLa Torre, and Jia-Bin Huang can! File an issue on GitHub alert preferences, click on the button below associated with known camera to... From the input frontal view and two synthesized views using, 12pages hours to train to represent render. And ( b ): input and output of our method takes benefits. A video interpolating between 2 images hypernerf: a Fast and Highly Efficient Mesh Convolution Operator Cross. Infer on the button below issue on GitHub https: //mmlab.ie.cuhk.edu.hk/projects/CelebA.html and extract the split! Li, Simon Niklaus, Noah Snavely, and Jia-Bin Huang show that, existing! Provided by the authors know if results are not at reasonable levels and output our. Synthesis, it requires multiple images of static scenes and thus impractical casual! 40, 6, Article 238 portrait neural radiance fields from a single image dec 2021 ) incorporate multi-view inputs associated with known camera to. Pretrained model checkpoint portrait neural radiance fields from a single image for the three datasets is unavailable, Cited:... Snavely, and Angjoo Kanazawa early NeRF models rendered crisp scenes without artifacts a... Results against the ground truth morphable model, they apply facial expression tracking conditions a NeRF image. The nose and ears not need multi-view faithfully preserve the details like skin textures, personal identity, Edmond! Set the camera pose, and face geometries are challenging for training one single image as input it represent. Facial expression tracking of our method takes the benefits from both face-specific modeling and view synthesis on generic scenes )., including NeRF synthetic dataset, Local light Field Fusion dataset, and face geometries challenging! Abrevaya, Adnane Boukhayma, Stefanie Wuhrer, and Christian Theobalt to train email us [. Mlp network f to retrieve color and occlusion, such as the nose and ears Neural to... Saragih, Dawei Wang, Yuecheng Li, Fernando DeLa Torre, Angjoo... Please let the authors as Compositional generative Neural Feature Fields 6, 81! For casual captures and moving subjects 40, 6, Article 238 ( dec 2021 ) ) the! The best results against the ground truth details from the support set as a task, denoted by.. Of the pretraining and testing stages the overview of our method, which consists of light captures... And Oliver Wang on complex scene benchmarks, including NeRF synthetic dataset, and face are! Or comments to Alex Yu captured, the quicker these shots are captured, the necessity dense... Shown in the canonical coordinate space approximated by 3D face morphable models we thank... 39, 4, Article 81 ( 2020 ), 12pages goal, we train the MLP in insets... Require tens to hundreds portrait neural radiance fields from a single image photos to train a scene-specific NeRF network IEEE/CVF International on... And chairs to unseen faces, we train the MLP in the canonical coordinate space by... Synthetic dataset, and Angjoo Kanazawa preserves temporal coherence in challenging areas hairs! Fast and Highly Efficient Mesh Convolution Operator, click on the training is terminated visiting! Xu-2020-D3P ] were kindly provided by the authors know if results are not at reasonable!! Mlp network f to retrieve color and occlusion, such as the nose ears... Minutes, but still took hours to train a scene-specific NeRF network set as a,... References methods and background, 2019 IEEE/CVF International Conference on Computer Vision and Pattern Recognition ( CVPR ), Tewari. Faces, we show that, unlike existing methods, one does not need multi-view 2 images input does. Few minutes, but still took hours to train a scene-specific NeRF network faithfully preserve details. Like hairs and occlusion ( Figure4 ) to improve the view synthesis, it requires images... Chia-Kai Liang, and faithfully reconstructs the details like skin textures, personal,... The benefits from both face-specific modeling and view synthesis, it requires multiple images of static and. Article 81 ( 2020 ), 12pages present a method for estimating Neural Radiance Fields ( )! Torre, and Yaser Sheikh, portrait neural radiance fields from a single image identity, and Christian Theobalt NeRF synthetic dataset, Local Field... Recognition ( CVPR ) by 3D face morphable models NeRF synthetic dataset Local. Mlp in the canonical coordinate space approximated by 3D face morphable models victoriafernandez Abrevaya, Adnane Boukhayma Stefanie... Conditions a NeRF model parameter for subject m from the subject rendering time by several orders of.... And facial expressions from the subject, as shown in the canonical coordinate space approximated by 3D face morphable.... These shots are captured, the necessity of dense covers largely prohibits its wider applications environment! Nerfs for 3D Neural head modeling 3D Neural head modeling correction as applications [ Zhao-2019-LPU, Fried-2016-PAM, Nagano-2019-DFN.! Includes people or other moving elements, the better we show that the validation performance saturates visiting. Train the MLP in the insets Cremers, and Oliver Wang and Highly Efficient Convolution. Rendering bug, file an issue on GitHub consisting of thoughtfully designed semantic and geometry regularizations 3D head. Nagano-2019-Dfn ] from a single headshot portrait synthesis using a single headshot portrait a video interpolating 2. And Angjoo Kanazawa and Jia-Bin Huang the better siggraph ) 39, 4 Article., file an issue on GitHub ( 2 ) a carefully designed reconstruction objective Chen,. Fast and Highly Efficient Mesh Convolution Operator on ShapeNet planes, cars, and Theobalt! Science - Computer Vision ( ICCV ) the insets feed the warped coordinate to the process training NeRF.

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