To learn the neural blend weight field wcan at the canonical space, we introduce a consistency loss between blend weight fields. Animatable Neural Radiance Fields for Human Body Modeling Given a multi-view video of a performer, our task is to reconstruct an animatable human model that can be used to synthesize free-viewpoint videos of the performer under novel human poses. Since the license of Human3.6M dataset does not allow us to distribute its data, we cannot release the processed Human3.6M dataset publicly. The parameters of new are optimized using. Sun, B. Schiele, T. Tuytelaars, and L. Van Gool, Y. Some recent works have proposed to decompose a non-rigidly deforming scene into a canonical neural radiance field and a set of deformation fields that map observation-space points to the canonical space, thereby enabling them to learn the dynamic scene from images. Recently, neural radiance fields (NeRF) [40] has proposed a representation that can be efficiently learned from images with a differentiable renderer. Animatable Neural Radiance Fields from Monocular RGB Videos First, high-quality human reconstruction generally relies on complicated hardware, such as a dense array of cameras [55, 16] or depth sensors [10, 14]. The reconstruction results are presented in the supplementary material. . Addtional training and test commandlines are recorded in train.sh and test.sh. This paper addresses the challenge of reconstructing an animatable human model from a multi-view video. skeletons to generate observation-to-canonical and canonical-to-observation dynamic neural field (dnf) model - 42Papers The overview of our approach is shown in Figure2. We provide the pretrained models at here. The Adam optimizer [25] is adopted for the training. Based on SMPL, some works [47, 23, 26, 20, 13] reconstruct an animated human mesh from sparse camera views. regularize the learning of deformation fields. In this work, we aim to reduce the cost of human reconstruction and animation, to enable the creation of digital humans at scale. awesome 3d human reconstruction Contents 3d human nerf or pifu StereoPIFu: Depth Aware Clothed Human Digitization via Stereo Vision paper Learning Implicit 3D Representations of Dressed Humans from Sparse Views paper Animatable Neural Radiance Fields for Human Body Modeling paper PaMIR: Parametric Model-Conditioned Implicit Representation for Image-based Human Reconstruction . Animatable Neural Radiance Fields for Human Body Modeling [54.41477114385557] This paper addresses the challenge of reconstructing an animatable human model from a multi-view video. Some recent works have proposed to decompose anon-rigidly deforming scene into a canonical neural radiance field and a set ofdeformation fields that map observation-space points to the canonical space,thereby enabling them to learn the dynamic scene from images. Animatable Neural Radiance Fields for Human Body Modeling First, since the human skeleton is easy to track [21], it does not need to be jointly optimized and thus provides an effective regularization on the learning of deformation fields. Qualitative results of SMPL and neural blend weight field, Comparison between neural blend weight field and SMPL blend weight field, Visualization of the residual vector field, Comparison between models trained with human poses, Results of models trained with different numbers of video frames, Qualitative results of models trained on poses, Comparison of models trained with different numbers of video frames, Results of models trained with different numbers of camera views, Comparison of models trained with different numbers of camera views, K. Aliev, A. Sevastopolsky, M. Kolos, D. Ulyanov, and V. Lempitsky, T. Alldieck, M. Magnor, B. L. Bhatnagar, C. Theobalt, and G. Pons-Moll, Learning to reconstruct people in clothing from a single RGB camera, T. Alldieck, M. Magnor, W. Xu, C. Theobalt, and G. Pons-Moll, Video based reconstruction of 3d people models, T. Alldieck, G. Pons-Moll, C. Theobalt, and M. Magnor, Tex2Shape: detailed full human body geometry from a single image, B. L. Bhatnagar, C. Sminchisescu, C. Theobalt, and G. Pons-Moll, Z. Cao, G. Hidalgo, T. Simon, S. Wei, and Y. Sheikh, OpenPose: realtime multi-person 2d pose estimation using part affinity fields, C. Chan, S. Ginosar, T. Zhou, and A. Based on the skeleton-driven deformation, blend weight fields are used with 3D human skeletons to generate observation-to-canonical and canonical-to-observation correspondences. To synthesize images of the performer under novel human poses, we similarly construct the deformation fields that transform the 3D points to the canonical space. 1State Key Lab of CAD & CG, Zhejiang University The code and supplementary materials are available at https://zju3dv.github.io/animatable_nerf/. 07/09/2022 We release the extended version of Animatable NeRF (now dubbed Animatable Neural Fields).We evaluated three different versions of Animatable Neural Fields, including vanilla Animatable NeRF, a version where the neural blend weight field is replaced with displacement field and a version where the canonical NeRF model is replaced with a neural surface field (output is SDF . The learning rate starts from 5e4 and decays exponentially to 5e5 along the optimization. Based on the advances in image-to-image translation techniques. A. Osman, D. Tzionas, and M. J. Based on blend weight fields, we are able to animate the canonical human model (Section3.4). This include training, evaluating and visualizing the original Animatable NeRF implementation and all three extented versions. : Neural body: implicit neural representations with structured latent codes for novel view synthesis of dynamic humans. Moreover, the learned blend weight fields can be combined with input skeletal motions to generate new deformation fields to animate the human model. To solve this problem, we seek the human priors in 3D statistical body models [35, 52, 46, 62] to regularize the learned blend weights. To overcome this problem, [3, 2, 4] apply vertex displacements to the SMPL model to capture the human clothing and hair. Neural Actor [neural_actors] shares a similar scheme with Animatable NeRF [peng2021animatable_nerf]: it also learns a neural radiance field in a canonical body pose, and use LBS to warp the canonical radiance field to represent the moving subject. Animatable Neural Radiance Fields for Human Body Modeling - ResearchGate approach significantly outperforms recent human synthesis methods. Animatable NeRFRGB (ICCV'21) - Tables 3, 4, 5, and 6 summarize the results of ablation studies. The command lines for training are recorded in train.sh. A. Efros, Image-to-image translation with conditional adversarial networks, W. Jiang, N. Kolotouros, G. Pavlakos, X. Zhou, and K. Daniilidis, Coherent reconstruction of multiple humans from a single image, Total capture: a 3d deformation model for tracking faces, hands, and bodies, A. Kanazawa, M. J. In this paper we present a novel representation for deformation fields o We present a mathematical model to decompose a longitudinal deformation Qualitative results of novel view synthesis on the H36M dataset. Impact of the video length. Since the license of the RenderPeople dataset does not allow distribution of the 3D model, we cannot realease the processed SyntheticHuman dataset publicly. - "Animatable Neural Radiance Fields for Human Body Modeling" As shown by equations (3) and (4), two corresponding points at canonical and observation spaces should have the same blend weights. Figure8 presents the qualitative comparisons. Are you sure you want to create this branch? Animatable Neural Radiance Fields for Modeling Dynamic Human Bodies 14314-14323 (2021) Google Scholar; 43. Animatable Neural Radiance Fields for Modeling Dynamic Human Bodies The network of Fw is almost the same as that of F, except that the final output layer of Fw has 24 channels. Recent implicit function-based methods [44, 39, 9] have exhibited state-of-the-art reconstruction quality. Moreover, as the sampled points are discretized, the calculated correspondences tend to be coarse. This paper addresses the challenge of reconstructing an animatable humanmodel from a multi-view video. Given a novel human pose, our method updates the pose parameters in the SMPL model and computes the SMPL blend weight field ws based on the new parameters Snew. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. More details of training and test data can be found in the supplementary material. [48] combines NeRF with the SMPL model, allowing it to handle dynamic humans and synthesize photorealistic novel views from very sparse camera views. Because the number of points sampled along the ray is only 64 and the scene bound of a human is small, the rendering speed of our method is relatively fast. First, we train the parameters of F, Fc, Fw, {i} and {i} jointly over the input video. Adam: A Method for Stochastic Optimization, Marching cubes: A high resolution 3D surface construction algorithm, Human3.6M: Large Scale Datasets and Predictive Methods for 3D Human Sensing in Natural Environments, IEEE Transactions on Pattern Analysis and Machine Intelligence, Pose space deformation: a unified approach to shape interpolation and skeleton-driven deformation, NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis, PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization, SMPL: a skinned multi-person linear model, Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations. In contrast, learning blend weights at observation spaces enables us to easily obtain the observation-to-canonical correspondences based on Equation (4). In particular, our model outperforms [59, 61] by 1.91 in terms of the PSNR metric and 0.02 in terms of the SSIM metric. Santiago - Wikipedia Moreover, these representations cannot be explicitly controlled by input motions. For both metrics, our method gives the best performances. In addition, Fw applies exp() to the output. Results of novel pose synthesis on H36M dataset in terms of PSNR and SSIM (higher is better). For 200 novel human poses, the second stage training takes around 10k iterations to converge (about 30 minutes). As shown in the second person of Figure3, they render the human back that is seen during training. Animatable Neural Radiance Fields for Modeling Dynamic Human Bodies: Project Page: 2021: ICCV: Torch: Neural . Specifically, for any 3D point, we assign an initial blend weight based on the body model and then use a network to learn a residual vector, resulting in the neural blend weight field. Since 3D human skeletons are more observable, they can regularize the learning of deformation fields. It first creates a scale-appropriate skeleton for the human mesh and then assigns each mesh vertex a blend weight that describes how the vertex position deforms with the skeleton. Impact of neural blend weight field. Download the corresponding pretrained models, and put it to $ROOT/data/trained_model/deform/aninerf_313/latest.pth and $ROOT/data/trained_model/deform/aninerf_313_full/latest.pth. Although they can handle some dynamic scenes, they are not suited for representing animatable human models due to two reasons. The command lines for training are recorded in train.sh. The parameters of F, Fc, Fw, {i} and {i} are jointly optimized over the multi-view video by minimizing the difference between the rendered pixel color ~Ci(r) and the observed pixel color Ci(r): where R is the set of rays passing through image pixels. For comparison, we synthesize novel views of training video frames. skeleton-driven deformation, blend weight fields are used with 3D human Neural inhibition during speech planning contributes to contrastive hyperarticulation. For any 3D point, we first find the closest surface point on the SMPL mesh. Our method augments a neural radiance field with deformation fields that transform observation-space points to the canonical space. Renca - CRL-2947 | ATCC Some recent works have proposed to decompose a non-rigidly deforming scene into a canonical neural radiance field and a set of deformation fields that map observation-space points to the canonical space, thereby enabling them to learn the dynamic scene from images. However, this problem is extremely challenging. In addition to synthesizing images under novel human poses, our approach can also explicitly animate a reconstructed human mesh, similar to the traditional animation methods. Video for paper "Animatable Neural Radiance Fields for Human Body Modeling"Website: https://zju3dv.github.io/animatable_nerf/arXiv: https://arxiv.org/abs/210. where wo(x) is the blend weight function defined in the observation space. Then, for any observation-space point, we can interpolate its corresponding canonical point based on the pre-computed correspondences. Moreover, the learned blend weight fields can be combined with input skeletal motions to generate new deformation fields to animate the human model. This representation has two advantages. The qualitative comparison is presented in Figure 7. We evaluate our approach on the H36M [18] dataset that captures dynamic humans in complex motions with synchronized cameras. The command lines for test are recorded in test.sh. Recent works proposed to represent a dynamic human body with shared canonical neural radiance fields which links to the observation space with deformation fields estimations.However, the learned canonical representation is static and the current design of the deformation . deformation fields to animate the human model. With the deformation field Tnew, our method uses equations (1) and (2) to produce the neural radiance field under the novel human pose. * denotes equal contribution. The cell was deposited by R Wiltrout in 1969. However, as discussed in [45, 29], optimizing a radiance field together with a deformation field is an ill-posed problem that is prone to local optima. Impact of the number of input views. Take the training on S9 as an example. model from a multi-view video. Instead of learning blend weight fields at both observation and canonical spaces, an alternative method is to only learn the blend weight field at the canonical space as in Equation (3), which specifies the canonical-to-observation correspondences. [17, 5] combine implicit function learning with the SMPL model to obtain detailed animatable human models. dynamic neural field - 42Papers This paper addresses the challenge of reconstructing an animatable human model from a multi-view video. : Animatable neural radiance fields for modeling dynamic human bodies. Training and test commandlines are recorded in train.sh and test.sh put it to ROOT/data/trained_model/deform/aninerf_313/latest.pth. Wiltrout in 1969 Torch: neural body: implicit neural representations with structured latent codes for novel view of. Motions to generate new deformation fields to animate the human model supplementary are! 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