2021. Therefore, we provide a script performing hybrid optimization: predict a latent code using our model, then perform latent optimization as introduced in pi-GAN. 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. 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. 2020. Explore our regional blogs and other social networks. A tag already exists with the provided branch name. We then feed the warped coordinate to the MLP network f to retrieve color and occlusion (Figure4). Graph. ICCV. This work introduces three objectives: a batch distribution loss that encourages the output distribution to match the distribution of the morphable model, a loopback loss that ensures the network can correctly reinterpret its own output, and a multi-view identity loss that compares the features of the predicted 3D face and the input photograph from multiple viewing angles. As a strength, we preserve the texture and geometry information of the subject across camera poses by using the 3D neural representation invariant to camera poses[Thies-2019-Deferred, Nguyen-2019-HUL] and taking advantage of pose-supervised training[Xu-2019-VIG]. Use Git or checkout with SVN using the web URL. 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). S. Gong, L. Chen, M. Bronstein, and S. Zafeiriou. Figure9(b) shows that such a pretraining approach can also learn geometry prior from the dataset but shows artifacts in view synthesis. by introducing an architecture that conditions a NeRF on image inputs in a fully convolutional manner. 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. 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. 33. Towards a complete 3D morphable model of the human head. CoRR abs/2012.05903 (2020), Copyright 2023 Sanghani Center for Artificial Intelligence and Data Analytics, Sanghani Center for Artificial Intelligence and Data Analytics. View 10 excerpts, references methods and background, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. SRN performs extremely poorly here due to the lack of a consistent canonical space. Abstract. IEEE, 44324441. We report the quantitative evaluation using PSNR, SSIM, and LPIPS[zhang2018unreasonable] against the ground truth inTable1. [Jackson-2017-LP3] only covers the face area. Single-Shot High-Quality Facial Geometry and Skin Appearance Capture. 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. Portrait Neural Radiance Fields from a Single Image Chen Gao, Yichang Shih, Wei-Sheng Lai, Chia-Kai Liang, and Jia-Bin Huang [Paper (PDF)] [Project page] (Coming soon) arXiv 2020 . We present a method for estimating Neural Radiance Fields (NeRF) from a single headshot portrait. 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. (c) Finetune. Since our method requires neither canonical space nor object-level information such as masks, Compared to the vanilla NeRF using random initialization[Mildenhall-2020-NRS], our pretraining method is highly beneficial when very few (1 or 2) inputs are available. Cited by: 2. We presented a method for portrait view synthesis using a single headshot photo. 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 Daniel Roich, Ron Mokady, AmitH Bermano, and Daniel Cohen-Or. We include challenging cases where subjects wear glasses, are partially occluded on faces, and show extreme facial expressions and curly hairstyles. Graphics (Proc. Portrait Neural Radiance Fields from a Single Image. InTable4, we show that the validation performance saturates after visiting 59 training tasks. Our data provide a way of quantitatively evaluating portrait view synthesis algorithms. The high diversities among the real-world subjects in identities, facial expressions, and face geometries are challenging for training. Our goal is to pretrain a NeRF model parameter p that can easily adapt to capturing the appearance and geometry of an unseen subject. In Proc. The subjects cover different genders, skin colors, races, hairstyles, and accessories. For each subject, While generating realistic images is no longer a difficult task, producing the corresponding 3D structure such that they can be rendered from different views is non-trivial. Non-Rigid Neural Radiance Fields: Reconstruction and Novel View Synthesis of a Dynamic Scene From Monocular Video. sign in Codebase based on https://github.com/kwea123/nerf_pl . Discussion. Inspired by the remarkable progress of neural radiance fields (NeRFs) in photo-realistic novel view synthesis of static scenes, extensions have been proposed for . 2021. Instant NeRF is a neural rendering model that learns a high-resolution 3D scene in seconds and can render images of that scene in a few milliseconds. arxiv:2110.09788[cs, eess], All Holdings within the ACM Digital Library. Tianye Li, Timo Bolkart, MichaelJ. inspired by, Parts of our We show the evaluations on different number of input views against the ground truth inFigure11 and comparisons to different initialization inTable5. Notice, Smithsonian Terms of We quantitatively evaluate the method using controlled captures and demonstrate the generalization to real portrait images, showing favorable results against state-of-the-arts. 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. In Proc. 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. By virtually moving the camera closer or further from the subject and adjusting the focal length correspondingly to preserve the face area, we demonstrate perspective effect manipulation using portrait NeRF inFigure8 and the supplemental video. In addition, we show thenovel application of a perceptual loss on the image space is critical forachieving photorealism. Jia-Bin Huang Virginia Tech Abstract We present a method for estimating Neural Radiance Fields (NeRF) from a single headshot portrait. Unlike previous few-shot NeRF approaches, our pipeline is unsupervised, capable of being trained with independent images without 3D, multi-view, or pose supervision. 2021b. 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. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 39, 5 (2020). We present a method for estimating Neural Radiance Fields (NeRF) from a single headshot portrait. Thu Nguyen-Phuoc, Chuan Li, Lucas Theis, Christian Richardt, and Yong-Liang Yang. Active Appearance Models. Since Dq is unseen during the test time, we feedback the gradients to the pretrained parameter p,m to improve generalization. CVPR. 345354. In Proc. 2021a. In Proc. In contrast, previous method shows inconsistent geometry when synthesizing novel views. Ablation study on canonical face coordinate. While NeRF has demonstrated high-quality view synthesis, it requires multiple images of static scenes and thus impractical for casual captures and moving subjects. Canonical face coordinate. Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. 2021. Our work is a first step toward the goal that makes NeRF practical with casual captures on hand-held devices. NeRF[Mildenhall-2020-NRS] represents the scene as a mapping F from the world coordinate and viewing direction to the color and occupancy using a compact MLP. Want to hear about new tools we're making? 2019. 2021. 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]. Using a new input encoding method, researchers can achieve high-quality results using a tiny neural network that runs rapidly. 44014410. CVPR. 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. The optimization iteratively updates the tm for Ns iterations as the following: where 0m=p,m1, m=Ns1m, and is the learning rate. 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). By clicking accept or continuing to use the site, you agree to the terms outlined in our. 2020. We first compute the rigid transform described inSection3.3 to map between the world and canonical coordinate. If nothing happens, download GitHub Desktop and try again. 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]. 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. 2020. SpiralNet++: A Fast and Highly Efficient Mesh Convolution Operator. [1/4] 01 Mar 2023 06:04:56 Pretraining on Ds. Guy Gafni, Justus Thies, Michael Zollhfer, and Matthias Niener. 40, 6 (dec 2021). arXiv preprint arXiv:2106.05744(2021). 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. 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. NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis. 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. Instant NeRF, however, cuts rendering time by several orders of magnitude. In the supplemental video, we hover the camera in the spiral path to demonstrate the 3D effect. 2021. It is thus impractical for portrait view synthesis because 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. The human head ( NeRF ) from a single headshot portrait figure9 ( b ) shows that such a approach. Previous method shows inconsistent geometry when synthesizing Novel views Radiance Fields ( )! Presented a method for estimating Neural Radiance Fields for view synthesis, it requires multiple of... Zhang2018Unreasonable ] against the ground truth inTable1 subjects wear glasses, are partially occluded on faces, and.. Report the quantitative portrait neural radiance fields from a single image using PSNR, SSIM, and show extreme facial expressions and curly hairstyles of! 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