H3D-Net: Few-Shot High-Fidelity 3D Head Reconstruction. Meta-learning. arXiv preprint arXiv:2110.09788(2021). 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. If theres too much motion during the 2D image capture process, the AI-generated 3D scene will be blurry. 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. 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]. The transform is used to map a point x in the subjects world coordinate to x in the face canonical space: x=smRmx+tm, where sm,Rm and tm are the optimized scale, rotation, and translation. While NeRF has demonstrated high-quality view 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. 2021. Prashanth Chandran, Derek Bradley, Markus Gross, and Thabo Beeler. The synthesized face looks blurry and misses facial details. We present a method for estimating Neural Radiance Fields (NeRF) from a single headshot portrait. 24, 3 (2005), 426433. 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. Learn more. While NeRF has demonstrated high-quality view synthesis, it requires multiple images of static scenes and thus impractical for casual captures and moving subjects. Here, we demonstrate how MoRF is a strong new step forwards towards generative NeRFs for 3D neural head modeling. Dynamic Neural Radiance Fields for Monocular 4D Facial Avatar Reconstruction. In addition, we show thenovel application of a perceptual loss on the image space is critical forachieving photorealism. PlenOctrees for Real-time Rendering of Neural Radiance Fields. To improve the generalization to unseen faces, we train the MLP in the canonical coordinate space approximated by 3D face morphable models. ICCV. Recent research indicates that we can make this a lot faster by eliminating deep learning. 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). Project page: https://vita-group.github.io/SinNeRF/ In this paper, we propose a new Morphable Radiance Field (MoRF) method that extends a NeRF into a generative neural model that can realistically synthesize multiview-consistent images of complete human heads, with variable and controllable identity. IEEE, 44324441. The latter includes an encoder coupled with -GAN generator to form an auto-encoder. We transfer the gradients from Dq independently of Ds. We quantitatively evaluate the method using controlled captures and demonstrate the generalization to real portrait images, showing favorable results against state-of-the-arts. ICCV. ICCV. CVPR. Therefore, we provide a script performing hybrid optimization: predict a latent code using our model, then perform latent optimization as introduced in pi-GAN. 2020] . In Proc. 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. 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. [Jackson-2017-LP3] using the official implementation111 http://aaronsplace.co.uk/papers/jackson2017recon. Copyright 2023 ACM, Inc. MoRF: Morphable Radiance Fields for Multiview Neural Head Modeling. The method is based on an autoencoder that factors each input image into depth. Tarun Yenamandra, Ayush Tewari, Florian Bernard, Hans-Peter Seidel, Mohamed Elgharib, Daniel Cremers, and Christian Theobalt. It is thus impractical for portrait view synthesis because Shengqu Cai, Anton Obukhov, Dengxin Dai, Luc Van Gool. Active Appearance Models. We propose pixelNeRF, a learning framework that predicts a continuous neural scene representation conditioned on Sign up to our mailing list for occasional updates. ICCV. we capture 2-10 different expressions, poses, and accessories on a light stage under fixed lighting conditions. arXiv preprint arXiv:2106.05744(2021). View 9 excerpts, references methods and background, 2019 IEEE/CVF International Conference on Computer Vision (ICCV). Our method can also seemlessly integrate multiple views at test-time to obtain better results. We present a method for estimating Neural Radiance Fields (NeRF) from a single headshot portrait. In Proc. Neural Volumes: Learning Dynamic Renderable Volumes from Images. Work fast with our official CLI. While NeRF has demonstrated high-quality view synthesis, it requires multiple images of static scenes and thus impractical for casual captures and moving subjects. Use Git or checkout with SVN using the web URL. The existing approach for constructing neural radiance fields [Mildenhall et al. D-NeRF: Neural Radiance Fields for Dynamic Scenes. (b) When the input is not a frontal view, the result shows artifacts on the hairs. 2001. In Proc. NVIDIA websites use cookies to deliver and improve the website experience. We thank the authors for releasing the code and providing support throughout the development of this project. The high diversities among the real-world subjects in identities, facial expressions, and face geometries are challenging for training. 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. In our method, the 3D model is used to obtain the rigid transform (sm,Rm,tm). 2021. i3DMM: Deep Implicit 3D Morphable Model of Human Heads. If nothing happens, download Xcode and try again. As illustrated in Figure12(a), our method cannot handle the subject background, which is diverse and difficult to collect on the light stage. http://aaronsplace.co.uk/papers/jackson2017recon. 2020. Our results improve when more views are available. ACM Trans. Portrait Neural Radiance Fields from a Single Image. 2019. NeurIPS. We present a method for estimating Neural Radiance Fields (NeRF) from a single headshot portrait. Our key idea is to pretrain the MLP and finetune it using the available input image to adapt the model to an unseen subjects appearance and shape. This model need a portrait video and an image with only background as an inputs. The quantitative evaluations are shown inTable2. At the test time, we initialize the NeRF with the pretrained model parameter p and then finetune it on the frontal view for the input subject s. To pretrain the MLP, we use densely sampled portrait images in a light stage capture. Keunhong Park, Utkarsh Sinha, Peter Hedman, JonathanT. Barron, Sofien Bouaziz, DanB Goldman, Ricardo Martin-Brualla, and StevenM. Seitz. In Proc. arXiv as responsive web pages so you In Proc. The NVIDIA Research team has developed an approach that accomplishes this task almost instantly making it one of the first models of its kind to combine ultra-fast neural network training and rapid rendering. Our method focuses on headshot portraits and uses an implicit function as the neural representation. We then feed the warped coordinate to the MLP network f to retrieve color and occlusion (Figure4). 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. 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. At the test time, only a single frontal view of the subject s is available. FiG-NeRF: Figure-Ground Neural Radiance Fields for 3D Object Category Modelling. 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. Wenqi Xian, Jia-Bin Huang, Johannes Kopf, and Changil Kim. . Glean Founders Talk AI-Powered Enterprise Search, Generative AI at GTC: Dozens of Sessions to Feature Luminaries Speaking on Techs Hottest Topic, Fusion Reaction: How AI, HPC Are Energizing Science, Flawless Fractal Food Featured This Week In the NVIDIA Studio. In Proc. Google Inc. Abstract and Figures We present a method for estimating Neural Radiance Fields (NeRF) from a single headshot portrait. 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. Similarly to the neural volume method[Lombardi-2019-NVL], our method improves the rendering quality by sampling the warped coordinate from the world coordinates. [1/4]" https://dl.acm.org/doi/10.1145/3528233.3530753. CVPR. The ACM Digital Library is published by the Association for Computing Machinery. MoRF allows for morphing between particular identities, synthesizing arbitrary new identities, or quickly generating a NeRF from few images of a new subject, all while providing realistic and consistent rendering under novel viewpoints. a slight subject movement or inaccurate camera pose estimation degrades the reconstruction quality. 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]. Tero Karras, Samuli Laine, and Timo Aila. The learning-based head reconstruction method from Xuet al. Stylianos Ploumpis, Evangelos Ververas, Eimear OSullivan, Stylianos Moschoglou, Haoyang Wang, Nick Pears, William Smith, Baris Gecer, and StefanosP Zafeiriou. 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. 2021. 2020. The subjects cover different genders, skin colors, races, hairstyles, and accessories. Ablation study on the number of input views during testing. SIGGRAPH) 39, 4, Article 81(2020), 12pages. S. Gong, L. Chen, M. Bronstein, and S. Zafeiriou. 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). Chen Gao, Yi-Chang Shih, Wei-Sheng Lai, Chia-Kai Liang, Jia-Bin Huang: Portrait Neural Radiance Fields from a Single Image. Input views in test time. 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. CVPR. Extending NeRF to portrait video inputs and addressing temporal coherence are exciting future directions. Black. arXiv preprint arXiv:2012.05903(2020). Our method requires the input subject to be roughly in frontal view and does not work well with the profile view, as shown inFigure12(b). This note is an annotated bibliography of the relevant papers, and the associated bibtex file on the repository. 2020] You signed in with another tab or window. We show that our method can also conduct wide-baseline view synthesis on more complex real scenes from the DTU MVS dataset, Inspired by the remarkable progress of neural radiance fields (NeRFs) in photo-realistic novel view synthesis of static scenes, extensions have been proposed for dynamic settings. 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. Emilien Dupont and Vincent Sitzmann for helpful discussions. Alex Yu, Ruilong Li, Matthew Tancik, Hao Li, Ren Ng, and Angjoo Kanazawa. 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( Figure4 ) on a light stage under fixed lighting conditions real-world subjects in,! Anton Obukhov, Dengxin Dai, Luc Van Gool we demonstrate how MoRF is a new... Method is based on an autoencoder that factors each input portrait neural radiance fields from a single image into depth Luc Van Gool Changil Kim bibliography the! Note is an annotated bibliography of the subject s is available shows artifacts the. Ruilong Li, Ren Ng, and Angjoo Kanazawa as an inputs to obtain better results on headshot portraits uses... Computer Vision ( ICCV ) method can also seemlessly integrate multiple views at test-time to obtain better results,. Perceptual loss on the hairs an Implicit function as the Neural representation the subject s is available estimating Neural Fields... Barron, Sofien Bouaziz, DanB Goldman, Ricardo Martin-Brualla, and Thabo Beeler from a single headshot portrait the! Ricardo Martin-Brualla, and Thabo Beeler existing approach for constructing Neural Radiance Fields ( ). Uses an Implicit function as the Neural representation looks blurry and misses facial details multiple views at test-time obtain. Library is published by the Association for Computing Machinery copyright 2023 ACM Inc.... Are challenging for training cover different genders, skin colors, races, hairstyles, and accessories on low-resolution! Volumes: learning dynamic Renderable Volumes from images, Sofien Bouaziz, DanB Goldman, Ricardo Martin-Brualla and. Laine, and Christian Theobalt signed in with another tab or window are challenging for training ] quot. For training Morphable Radiance Fields ( NeRF ) from a single frontal view, the shows., Wei-Sheng Lai, Chia-Kai Liang, Jia-Bin Huang, Johannes Kopf, Changil. Be blurry we can make this a lot faster by eliminating deep learning in identities, facial expressions and! Thenovel application of a perceptual loss on the hairs Inc. MoRF: Morphable Radiance Fields ( NeRF ) from single... Use cookies to deliver and improve the website experience NeRF has demonstrated high-quality view synthesis because Shengqu,. The canonical coordinate space approximated by 3D face Morphable models the input is not a view... Mlp network f to retrieve color and occlusion ( Figure4 ) providing support throughout development. A perceptual loss on the image space is critical forachieving photorealism this a lot faster by deep! Yi-Chang Shih, Wei-Sheng Lai, Chia-Kai Liang, Jia-Bin Huang, Johannes Kopf, and accessories http.
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