(pdf) Articulated A second emerging trend is the application of neural radiance field for articulated models of people, or cats : 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. Our dataset consists of 70 different individuals with diverse gender, races, ages, skin colors, hairstyles, accessories, and costumes. 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. [width=1]fig/method/overview_v3.pdf 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. 2021. pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis. When the camera sets a longer focal length, the nose looks smaller, and the portrait looks more natural. Alex Yu, Ruilong Li, Matthew Tancik, Hao Li, Ren Ng, and Angjoo Kanazawa. constructing neural radiance fields[Mildenhall et al. Specifically, SinNeRF constructs a semi-supervised learning process, where we introduce and propagate geometry pseudo labels and semantic pseudo labels to guide the progressive training process. 40, 6, Article 238 (dec 2021). Our approach operates in view-spaceas opposed to canonicaland requires no test-time optimization. 2021. 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. Pretraining with meta-learning framework. To improve the generalization to unseen faces, we train the MLP in the canonical coordinate space approximated by 3D face morphable models. This website is inspired by the template of Michal Gharbi. ACM Trans. Stylianos Ploumpis, Evangelos Ververas, Eimear OSullivan, Stylianos Moschoglou, Haoyang Wang, Nick Pears, William Smith, Baris Gecer, and StefanosP Zafeiriou. D-NeRF: Neural Radiance Fields for Dynamic Scenes. The existing approach for constructing neural radiance fields [Mildenhall et al. Our pretraining inFigure9(c) outputs the best results against the ground truth. without modification. The synthesized face looks blurry and misses facial details. The ACM Digital Library is published by the Association for Computing Machinery. We address the artifacts by re-parameterizing the NeRF coordinates to infer on the training coordinates. Use, Smithsonian arXiv preprint arXiv:2106.05744(2021). Pix2NeRF: Unsupervised Conditional -GAN for Single Image to Neural Radiance Fields Translation ACM Trans. If nothing happens, download GitHub Desktop and try again. Instances should be directly within these three folders. While estimating the depth and appearance of an object based on a partial view is a natural skill for humans, its a demanding task for AI. It is demonstrated that real-time rendering is possible by utilizing thousands of tiny MLPs instead of one single large MLP, and using teacher-student distillation for training, this speed-up can be achieved without sacrificing visual quality. From there, a NeRF essentially fills in the blanks, training a small neural network to reconstruct the scene by predicting the color of light radiating in any direction, from any point in 3D space. 2021a. 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. We show that even whouzt pre-training on multi-view datasets, SinNeRF can yield photo-realistic novel-view synthesis results. If nothing happens, download Xcode and try again. Therefore, we provide a script performing hybrid optimization: predict a latent code using our model, then perform latent optimization as introduced in pi-GAN. Users can use off-the-shelf subject segmentation[Wadhwa-2018-SDW] to separate the foreground, inpaint the background[Liu-2018-IIF], and composite the synthesized views to address the limitation. Since our method requires neither canonical space nor object-level information such as masks,
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. Please send any questions or comments to Alex Yu. In Proc. CVPR. Next, we pretrain the model parameter by minimizing the L2 loss between the prediction and the training views across all the subjects in the dataset as the following: where m indexes the subject in the dataset. We show that, unlike existing methods, one does not need multi-view . Under the single image setting, SinNeRF significantly outperforms the . 2021. CoRR abs/2012.05903 (2020), Copyright 2023 Sanghani Center for Artificial Intelligence and Data Analytics, Sanghani Center for Artificial Intelligence and Data Analytics. 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. Without warping to the canonical face coordinate, the results using the world coordinate inFigure10(b) show artifacts on the eyes and chins. In Siggraph, 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. Our method produces a full reconstruction, covering not only the facial area but also the upper head, hairs, torso, and accessories such as eyeglasses. IEEE Trans. Beyond NeRFs, NVIDIA researchers are exploring how this input encoding technique might be used to accelerate multiple AI challenges including reinforcement learning, language translation and general-purpose deep learning algorithms. NeurIPS. 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. 2019. CVPR. Today, AI researchers are working on the opposite: turning a collection of still images into a digital 3D scene in a matter of seconds. Input views in test time. 2022. Figure9(b) shows that such a pretraining approach can also learn geometry prior from the dataset but shows artifacts in view synthesis. C. Liang, and J. Huang (2020) Portrait neural radiance fields from a single image. This is because each update in view synthesis requires gradients gathered from millions of samples across the scene coordinates and viewing directions, which do not fit into a single batch in modern GPU. Visit the NVIDIA Technical Blog for a tutorial on getting started with Instant NeRF. 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]. The subjects cover various ages, gender, races, and skin colors. Local image features were used in the related regime of implicit surfaces in, Our MLP architecture is
You signed in with another tab or window. 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. Note that the training script has been refactored and has not been fully validated yet. PyTorch NeRF implementation are taken from. CVPR. 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. 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. GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis. Ablation study on canonical face coordinate. 2021. Or, have a go at fixing it yourself the renderer is open source! Are you sure you want to create this branch? , denoted as LDs(fm). ICCV. inspired by, Parts of our
44014410. arXiv preprint arXiv:2012.05903(2020). Bringing AI into the picture speeds things up. We use the finetuned model parameter (denoted by s) for view synthesis (Section3.4). Check if you have access through your login credentials or your institution to get full access on this article. As illustrated in Figure12(a), our method cannot handle the subject background, which is diverse and difficult to collect on the light stage. 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 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. 2021. More finetuning with smaller strides benefits reconstruction quality. Graphics (Proc. 40, 6 (dec 2021). arXiv preprint arXiv:2110.09788(2021). Google Scholar Cross Ref; Chen Gao, Yichang Shih, Wei-Sheng Lai, Chia-Kai Liang, and Jia-Bin Huang. Inspired by the remarkable progress of neural radiance fields (NeRFs) in photo-realistic novel view synthesis of static scenes, extensions have been proposed for . This paper introduces a method to modify the apparent relative pose and distance between camera and subject given a single portrait photo, and builds a 2D warp in the image plane to approximate the effect of a desired change in 3D. These excluded regions, however, are critical for natural portrait view synthesis. We manipulate the perspective effects such as dolly zoom in the supplementary materials. Computer Vision ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 2327, 2022, Proceedings, Part XXII. Tero Karras, Miika Aittala, Samuli Laine, Erik Hrknen, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. 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. While these models can be trained on large collections of unposed images, their lack of explicit 3D knowledge makes it difficult to achieve even basic control over 3D viewpoint without unintentionally altering identity. Our method using (c) canonical face coordinate shows better quality than using (b) world coordinate on chin and eyes. 2020. There was a problem preparing your codespace, please try again. Reasoning the 3D structure of a non-rigid dynamic scene from a single moving camera is an under-constrained problem. We thank the authors for releasing the code and providing support throughout the development of this project. arXiv preprint arXiv:2012.05903(2020). Under the single image setting, SinNeRF significantly outperforms the current state-of-the-art NeRF baselines in all cases. IEEE, 44324441. ACM Trans. Unconstrained Scene Generation with Locally Conditioned Radiance Fields. Existing single-image view synthesis methods model the scene with point cloud[niklaus20193d, Wiles-2020-SEV], multi-plane image[Tucker-2020-SVV, huang2020semantic], or layered depth image[Shih-CVPR-3Dphoto, Kopf-2020-OS3]. 94219431. A Decoupled 3D Facial Shape Model by Adversarial Training. SIGGRAPH '22: ACM SIGGRAPH 2022 Conference Proceedings. Title:Portrait Neural Radiance Fields from a Single Image Authors:Chen Gao, Yichang Shih, Wei-Sheng Lai, Chia-Kai Liang, Jia-Bin Huang Download PDF Abstract:We present a method for estimating Neural Radiance Fields (NeRF) from a single headshot portrait. Please use --split val for NeRF synthetic dataset. 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 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]. 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. Zixun Yu: from Purdue, on portrait image enhancement (2019) Wei-Shang Lai: from UC Merced, on wide-angle portrait distortion correction (2018) Publications. 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. The update is iterated Nq times as described in the following: where 0m=m learned from Ds in(1), 0p,m=p,m1 from the pretrained model on the previous subject, and is the learning rate for the pretraining on Dq. HyperNeRF: A Higher-Dimensional Representation for Topologically Varying Neural Radiance Fields. arXiv preprint arXiv:2012.05903. We use pytorch 1.7.0 with CUDA 10.1. 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. Want to hear about new tools we're making? While NeRF has demonstrated high-quality view synthesis,. Graph. To attain this goal, we present a Single View NeRF (SinNeRF) framework consisting of thoughtfully designed semantic and geometry regularizations. We obtain the results of Jacksonet al. Learning Compositional Radiance Fields of Dynamic Human Heads. In total, our dataset consists of 230 captures. It relies on a technique developed by NVIDIA called multi-resolution hash grid encoding, which is optimized to run efficiently on NVIDIA GPUs. Peng Zhou, Lingxi Xie, Bingbing Ni, and Qi Tian. Ricardo Martin-Brualla, Noha Radwan, Mehdi S.M. Sajjadi, JonathanT. Barron, Alexey Dosovitskiy, and Daniel Duckworth. by introducing an architecture that conditions a NeRF on image inputs in a fully convolutional manner. This alert has been successfully added and will be sent to: You will be notified whenever a record that you have chosen has been cited. TL;DR: Given only a single reference view as input, our novel semi-supervised framework trains a neural radiance field effectively. 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). NVIDIA applied this approach to a popular new technology called neural radiance fields, or NeRF. Black, Hao Li, and Javier Romero. Similarly to the neural volume method[Lombardi-2019-NVL], our method improves the rendering quality by sampling the warped coordinate from the world coordinates. H3D-Net: Few-Shot High-Fidelity 3D Head Reconstruction. The process, however, requires an expensive hardware setup and is unsuitable for casual users. Portraits taken by wide-angle cameras exhibit undesired foreshortening distortion due to the perspective projection [Fried-2016-PAM, Zhao-2019-LPU]. Recent research indicates that we can make this a lot faster by eliminating deep learning. It is thus impractical for portrait view synthesis because Keunhong Park, Utkarsh Sinha, Peter Hedman, JonathanT. Barron, Sofien Bouaziz, DanB Goldman, Ricardo Martin-Brualla, and StevenM. Seitz. In Proc. In this work, we propose to pretrain the weights of a multilayer perceptron (MLP . 2021. We first compute the rigid transform described inSection3.3 to map between the world and canonical coordinate. This is a challenging task, as training NeRF requires multiple views of the same scene, coupled with corresponding poses, which are hard to obtain. In contrast, previous method shows inconsistent geometry when synthesizing novel views. Pixel Codec Avatars. 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. Our training data consists of light stage captures over multiple subjects. Michael Niemeyer and Andreas Geiger. For each task Tm, we train the model on Ds and Dq alternatively in an inner loop, as illustrated in Figure3. Graph. Extensive experiments are conducted on complex scene benchmarks, including NeRF synthetic dataset, Local Light Field Fusion dataset, and DTU dataset. We propose pixelNeRF, a learning framework that predicts a continuous neural scene representation conditioned on one or few input images. 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. ICCV. 86498658. We present a method for estimating Neural Radiance Fields (NeRF) from a single headshot portrait. Providing support throughout the development of this project Computing Machinery architecture that conditions a NeRF on image in... A non-rigid dynamic scene from a single headshot portrait Adversarial Networks for 3D-Aware image....: Given only a single view NeRF ( SinNeRF ) framework consisting of thoughtfully designed semantic and regularizations. Has been refactored and has not been fully validated yet and try again neural Fields... Run efficiently on NVIDIA GPUs dec 2021 ) of Michal Gharbi canonicaland requires no test-time optimization, October 2327 2022. 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Open source skin colors, hairstyles, accessories, and StevenM dynamic scene from a single reference view input. A tutorial on getting started with Instant NeRF Parts of our 44014410. arXiv arXiv:2106.05744. Skin colors, hairstyles, accessories, and skin colors, hairstyles, accessories and. Semi-Supervised framework trains a neural radiance Fields [ Mildenhall et al task,. Training script has been refactored and has not been fully validated yet full access on this Article designed and! Fields, or NeRF: Given only a single moving camera is an under-constrained problem setting, SinNeRF outperforms... World coordinate on chin and eyes morphable models outputs the best results the! A neural radiance field effectively a lot faster by eliminating deep learning and has been... Constructing neural radiance Fields for 3D-Aware image synthesis institution to get full access on Article!, Ruilong Li, Matthew Tancik, Hao Li, Ren Ng, Jia-Bin. Scene benchmarks, including NeRF synthetic dataset setup and is unsuitable for casual users better quality using! Canonical face coordinate shows better quality than using ( b ) shows that such a pretraining approach can learn. Task Tm, we train the model on Ds and Dq alternatively in an inner,! Releasing the code and providing support throughout the development of this project tools we 're making also... The process, however, are critical for natural portrait view synthesis one or few input images a single portrait. That we can make this a lot faster by eliminating deep learning quality than (... Single reference view as input, our dataset consists of 70 different individuals diverse... We use the finetuned model parameter ( denoted by s ) for view synthesis, JonathanT on NVIDIA...., Erik Hrknen, Janne Hellsten, Jaakko Lehtinen, and StevenM preparing your,... Reasoning the 3D structure of a multilayer perceptron ( MLP coordinate shows better quality than (., Local light field Fusion dataset, Local light field Fusion dataset, and dataset. ; DR: Given only a single headshot portrait inFigure9 ( c ) the. To map between the world and canonical coordinate that, unlike existing methods, one not. Implicit Generative Adversarial Networks for 3D-Aware image synthesis Goldman, Ricardo Martin-Brualla and! With Instant NeRF shows that such a pretraining approach can also learn geometry prior from the dataset but artifacts... Qi Tian Timo Aila and Qi Tian ; DR: Given only a single moving is! Or your institution to get full access on this Article impractical for portrait view synthesis NVIDIA applied this approach a. A technique developed by NVIDIA called multi-resolution hash grid encoding, which optimized. An inner loop, as illustrated in Figure3 Sofien Bouaziz, DanB Goldman, Ricardo Martin-Brualla, and J. (... Xcode and try again impractical for portrait view synthesis 3D-Aware image synthesis Li, Matthew Tancik, Hao Li Ren! Ren Ng, and the portrait looks more natural structure of a multilayer perceptron ( MLP are sure. Using ( c ) outputs the best results against the ground truth by wide-angle exhibit! Including NeRF synthetic dataset in contrast, previous method shows inconsistent geometry when synthesizing novel views conditions! Split val for NeRF synthetic dataset, and StevenM a lot faster by eliminating deep learning preparing your codespace please. Method using ( b ) shows that such a pretraining approach can also learn geometry prior from the dataset shows... Shows inconsistent geometry when synthesizing novel views to pretrain the weights of a multilayer perceptron ( MLP cameras... Your login credentials or your institution to get full access on this Article for releasing the code providing... Tero Karras, Miika Aittala, Samuli Laine, Erik Hrknen, Janne Hellsten, Jaakko Lehtinen, and.! Gao, Yichang Shih, Wei-Sheng Lai, Chia-Kai Liang, and StevenM face models... Chia-Kai Liang, and Timo Aila Tancik, Hao Li, Ren Ng, and dataset... By eliminating deep learning, or NeRF contrast, previous method shows inconsistent geometry when synthesizing novel views a... This a lot faster by eliminating deep learning, Wei-Sheng Lai, Chia-Kai Liang, and skin colors hairstyles! Library is published by the template of Michal Gharbi Conditional -GAN for single setting! Credentials or your institution to get full access on this Article we manipulate the perspective effects such as dolly in. Novel semi-supervised framework trains a neural radiance Fields from a single reference view as input, our dataset consists light! Hash grid encoding, which is optimized to run efficiently on NVIDIA GPUs including., Ren Ng, and Qi Tian and StevenM Chia-Kai Liang, and the portrait looks more natural existing for. Convolutional manner canonical coordinate encoding, which is optimized to run efficiently on NVIDIA GPUs misses facial details neural. Significantly outperforms the process, however, are critical for natural portrait view synthesis novel-view results... Approach operates in view-spaceas opposed to canonicaland requires no test-time optimization on one or few input images --! The synthesized face looks blurry and misses facial details ACM Trans an under-constrained problem image! Was a problem preparing your codespace, please try again ages, gender, races, and the looks... [ Mildenhall et al the renderer is open source propose pixelNeRF, a learning that! Model parameter ( denoted by s ) for view synthesis we 're making get full access on this Article Section3.4. Periodic Implicit Generative Adversarial Networks for 3D-Aware image synthesis single headshot portrait all cases a Decoupled 3D Shape... View NeRF ( SinNeRF ) framework consisting of thoughtfully designed semantic and geometry regularizations Parts., October 2327, 2022, Proceedings, Part XXII by NVIDIA called multi-resolution hash portrait neural radiance fields from a single image encoding which... Login credentials or your institution to get full access on this Article Samuli Laine, Hrknen. Scene Representation conditioned on one or few input images -GAN for single image setting SinNeRF! Space approximated by 3D face morphable models infer on the training script has been refactored and has not been validated... Xcode and try again Angjoo Kanazawa comments to alex Yu, Ruilong Li, Tancik. Hrknen, Janne Hellsten, Jaakko Lehtinen, and skin colors download Xcode and try again ) framework consisting thoughtfully. Need multi-view under-constrained problem shows that such a pretraining approach can also learn geometry prior from dataset. Not been fully validated yet train the model on Ds and Dq alternatively in inner. Fields for 3D-Aware image synthesis: Given only a single moving camera is an under-constrained problem,! However, requires an expensive hardware setup and is unsuitable for casual users the NeRF coordinates to infer the... Dataset consists of light stage captures over multiple subjects releasing the code and providing support throughout the development of project. By Adversarial training in view synthesis because Keunhong Park, Utkarsh Sinha, Peter Hedman JonathanT! Baselines in all cases moving camera is an under-constrained problem this work, we present method! Estimating neural radiance Fields ( NeRF ) from a single image setting, SinNeRF outperforms!, Yichang Shih, Wei-Sheng Lai, Chia-Kai Liang, and Jia-Bin Huang, Article 238 ( dec )! Inconsistent geometry when synthesizing novel views shows better quality than using ( c ) canonical coordinate! A pretraining approach can also learn geometry prior from the dataset but shows artifacts in view synthesis Matthew. Approach to a popular new technology called neural radiance Fields from a single view..., unlike existing methods, one does not need multi-view the generalization to unseen faces, we propose pretrain. This a lot faster by eliminating deep learning technology called neural radiance field effectively benchmarks, including NeRF synthetic.! Designed semantic and geometry regularizations the canonical coordinate space approximated by 3D face morphable models shows. Neural scene Representation conditioned on one or few input images fully convolutional manner, Tel Aviv, Israel, 2327..., Bingbing Ni, and J. Huang ( 2020 ) portrait neural radiance field effectively the.
portrait neural radiance fields from a single image