Tex2Shape: Detailed Full Human Body Geometry From a Single Image

We present a simple yet effective method to infer detailed full human body shape from only a single photograph. Our model can infer full-body shape including face, hair, and clothing including wrinkles at interactive frame-rates. Results feature details even on parts that are occluded in the input image. Our main idea is to turn shape regression into an aligned image-to-image translation problem. The input to our method is a partial texture map of the visible region obtained from off-the-shelf methods. From a partial texture, we estimate detailed normal and vector displacement maps, which can be applied to a low-resolution smooth body model to add detail and clothing. Despite being trained purely with synthetic data, our model generalizes well to real-world photographs. Numerous results demonstrate the versatility and robustness of our method

Learning to Reconstruct People in Clothing from a Single RGB Camera

We present a learning-based model to infer the personalized 3D shape of people from a few frames (1-8) of a monocular video in which the person is moving, in less than 10 seconds with a reconstruction accuracy of 5mm. Our model learns to predict the parameters of a statistical body model and instance displacements that add clothing and hair to the shape. The model achieves fast and accurate predictions based on two key design choices. First, by predicting shape in a canonical T-pose space, the network learns to encode the images of the person into pose-invariant latent codes, where the information is fused. Second, based on the observation that feed-forward predictions are fast but do not always align with the input images, we predict using both, bottom-up and top-down streams (one per view) allowing information to flow in both directions. Learning relies only on synthetic 3D data. Once learned, the model can take a variable number of frames as input, and is able to reconstruct shapes even from a single image with an accuracy of 6mm. Results on 3 different datasets demonstrate the efficacy and accuracy of our approach

TINJAUAN SEJARAH K.H. MOH. BAQIR ADELAN DALAM MENGEMBANGKAN ENTERPRENEURSHIP DI PONDOK PESANTREN TARBIYATUT THOLABAH KRANJI PACIRAN LAMONGAN TAHUN 1958-1990

Masalah yang diteliti dalam penulisan skripsi ini adalah (1) Bagaimana biografi K.H. Moh. Baqir Adelan? (2) Bagaimana profil Pondok Pesantren Tarbiyatut Tholabah yang dipimpin oleh K.H. Moh. Baqir Adelan? (3) Bagaimana usaha K.H. Moh. Baqir Adelan dalam mengembangkan enterpreneurship di Pondok Pesantren Tarbiyatut Tholabah tahun 1958-1990? Untuk menjawab permasalahan tersebut, penulis menggunakan metode penelitian sejarah, yang terdiri dari beberapa tahapan yaitu (1) heuristik adalah pengumpulan data yang terdiri dari sumber benda maupun lisan serta sumber buku-buku yang berkaitan dengan penelitian ini. (2) kritik. (3) interpretasi. (4) historiografi. Adapun pendekatan yang digunakan yaitu pendekatan historis yang mendiskripsikan peristiwa yang terjadi pada masa lampau. Dalam hal ini peneliti menggunakan teori sejarah naratif, yang dibawakan oleh K.H. Moh. Baqir Adelan seorang pelaku dalam panggung sandiwara dan teori continuity and change yang dikutip oleh Zamakhsyari Dhofier. Dari penelitian ini dapat disimpulkan bahwa (1) K.H. Moh. Baqir Adelan lahir pada tanggal 30 Agustus 1934. Beliau menuntut ilmu pertama kali di Madrasah Salafi Tarbiyatut Tholabah dan melanjutkan ke Pondok Al-Amin Tunggul yang dioleh K.H. Amin Musthofa, Paman beliau sendiri. setelah itu selama enam tahun beliau pergi ke Jombang untuk mondok di Tambakberas selama 2 tahun dan Denanyar selama 4 tahun. pada tahun 1958 beliau kembali ke pondok. Beliau menjadi pengasuh Pondok Pesantre Tarbiyatut Tholabah pada tahun 1976. Beliau wafat pada usia 72 tahun yang bertepatan dengan tanggal 15 Mei 2006. (2) pondok pesantren Tarbiyatut Tholabah didirikan tahun 1898 M. di desa Kranji Paciran Lamongan. pondok Tarbiyatut Tholabah didirikan oleh K.H. Musthofa. Dulu pondok tersebut hanya terdapat berupa asrama dan masjid saja, namun dengan perkembangan zaman yang menuntut akan adanya lembaga formal untuk memenuhi intruksi dari kementrian pendidikan. Sekarang, pondok tersebut mempunyai lembaga formal diantaranya adalah: MI, MTs, MA dan STAIDRA. (3) selain seorang kiai, K.H. Moh. Baqir Adelan adalah seorang yang mempunyai jiwa enterpreneurship. Hal ini dibuktikan dengan usaha beliau dalam mendirikan UD. Barokah Sejati. Bukan hanya itu, sebelum mempunyai usaha meubel (UD. Barokah Sejati) beliau sudah mempunyai usaha dalam bidang penyedia kitab-kitab yang dibutuhkan oleh lembaga ma’arif di daerah Paciran

Learning 3D Human Pose from Structure and Motion

3D human pose estimation from a single image is a challenging problem, especially for in-the-wild settings due to the lack of 3D annotated data. We propose two anatomically inspired loss functions and use them with a weakly-supervised learning framework to jointly learn from large-scale in-the-wild 2D and indoor/synthetic 3D data. We also present a simple temporal network that exploits temporal and structural cues present in predicted pose sequences to temporally harmonize the pose estimations. We carefully analyze the proposed contributions through loss surface visualizations and sensitivity analysis to facilitate deeper understanding of their working mechanism. Our complete pipeline improves the state-of-the-art by 11.8% and 12% on Human3.6M and MPI-INF-3DHP, respectively, and runs at 30 FPS on a commodity graphics card.Comment: ECCV 2018. Project page: https://www.cse.iitb.ac.in/~rdabral/3DPose

{SelfPose}: {3D} Egocentric Pose Estimation from a Headset Mounted Camera

We present a solution to egocentric 3D body pose estimation from monocular images captured from downward looking fish-eye cameras installed on the rim of a head mounted VR device. This unusual viewpoint leads to images with unique visual appearance, with severe self-occlusions and perspective distortions that result in drastic differences in resolution between lower and upper body. We propose an encoder-decoder architecture with a novel multi-branch decoder designed to account for the varying uncertainty in 2D predictions. The quantitative evaluation, on synthetic and real-world datasets, shows that our strategy leads to substantial improvements in accuracy over state of the art egocentric approaches. To tackle the lack of labelled data we also introduced a large photo-realistic synthetic dataset. xR-EgoPose offers high quality renderings of people with diverse skintones, body shapes and clothing, performing a range of actions. Our experiments show that the high variability in our new synthetic training corpus leads to good generalization to real world footage and to state of theart results on real world datasets with ground truth. Moreover, an evaluation on the Human3.6M benchmark shows that the performance of our method is on par with top performing approaches on the more classic problem of 3D human pose from a third person viewpoint.Comment: 14 pages. arXiv admin note: substantial text overlap with arXiv:1907.1004

Implicit Functions in Feature Space for {3D} Shape Reconstruction and Completion

While many works focus on 3D reconstruction from images, in this paper, we focus on 3D shape reconstruction and completion from a variety of 3D inputs, which are deficient in some respect: low and high resolution voxels, sparse and dense point clouds, complete or incomplete. Processing of such 3D inputs is an increasingly important problem as they are the output of 3D scanners, which are becoming more accessible, and are the intermediate output of 3D computer vision algorithms. Recently, learned implicit functions have shown great promise as they produce continuous reconstructions. However, we identified two limitations in reconstruction from 3D inputs: 1) details present in the input data are not retained, and 2) poor reconstruction of articulated humans. To solve this, we propose Implicit Feature Networks (IF-Nets), which deliver continuous outputs, can handle multiple topologies, and complete shapes for missing or sparse input data retaining the nice properties of recent learned implicit functions, but critically they can also retain detail when it is present in the input data, and can reconstruct articulated humans. Our work differs from prior work in two crucial aspects. First, instead of using a single vector to encode a 3D shape, we extract a learnable 3-dimensional multi-scale tensor of deep features, which is aligned with the original Euclidean space embedding the shape. Second, instead of classifying x-y-z point coordinates directly, we classify deep features extracted from the tensor at a continuous query point. We show that this forces our model to make decisions based on global and local shape structure, as opposed to point coordinates, which are arbitrary under Euclidean transformations. Experiments demonstrate that IF-Nets clearly outperform prior work in 3D object reconstruction in ShapeNet, and obtain significantly more accurate 3D human reconstructions.Comment: {IEEE} Conference on Computer Vision and Pattern Recognition (CVPR)202

{Tex2Shape}: Detailed Full Human Body Geometry from a Single Image

We present a simple yet effective method to infer detailed full human body shape from only a single photograph. Our model can infer full-body shape including face, hair, and clothing including wrinkles at interactive frame-rates. Results feature details even on parts that are occluded in the input image. Our main idea is to turn shape regression into an aligned image-to-image translation problem. The input to our method is a partial texture map of the visible region obtained from off-the-shelf methods. From a partial texture, we estimate detailed normal and vector displacement maps, which can be applied to a low-resolution smooth body model to add detail and clothing. Despite being trained purely with synthetic data, our model generalizes well to real-world photographs. Numerous results demonstrate the versatility and robustness of our method

Detailed Human Avatars from Monocular Video

We present a novel method for high detail-preserving human avatar creation from monocular video. A parameterized body model is refined and optimized to maximally resemble subjects from a video showing them from all sides. Our avatars feature a natural face, hairstyle, clothes with garment wrinkles, and high-resolution texture. Our paper contributes facial landmark and shading-based human body shape refinement, a semantic texture prior, and a novel texture stitching strategy, resulting in the most sophisticated-looking human avatars obtained from a single video to date. Numerous results show the robustness and versatility of our method. A user study illustrates its superiority over the state-of-the-art in terms of identity preservation, level of detail, realism, and overall user preference