3d pose program

Author: C | 2025-04-25

Poser is a 3D computer graphics program for posing, animating, and rendering In this article, we will explore the top 10 character posing software programs that are popular among animators and illustrators. 1. Daz 3D. Daz 3D is a versatile character posing

DesignDoll - free 3D posing program

Depth Prediction3DV 2020Learning Monocular Dense Depth from EventsICCV 2019Learning an Event Sequence Embedding for Dense Event-Based Deep Stereo4 Domain Specific4.1 NeRF & 3D reconstructionPublicationTitleHighlight3DV 20243d pose estimation of two interacting hands from a monocular event cameraArxiv 2022EventNeRF: Neural Radiance Fields from a Single Colour Event CameraArxiv 2022Ev-NeRF: Event Based Neural Radiance FieldArxiv 2022E-NeRF: Neural Radiance Fields from a Moving Event CameraIJCV 2018EMVS: Event-Based Multi-View Stereo—3D Reconstruction with an Event Camera in Real-TimeArxiv 2020E3D: Event-Based 3D Shape ReconstructionECCV 2022EvAC3D: From Event-based Apparent Contours to 3D Models via Continuous Visual HullsArxiv 2022Event-based Non-Rigid Reconstruction from ContoursArxiv 2022Event-Based Dense Reconstruction PipelineECCV 2016Real-Time 3D Reconstruction and 6-DoF Tracking with an Event CameraICAR 2019Multi-View 3D Reconstruction with Self-Organizing Maps on Event-Based DataIEEE 2018Ultimate slam? combining events, images, and imu for robust visual slam in hdr and high-speed scenarios3DV 2021ESL: Event-based Structured LightECCV 2020Stereo Event-Based Particle Tracking Velocimetry for 3D Fluid Flow ReconstructionICCV 2019Learning an Event Sequence Embedding for Dense Event-Based Deep Stereo4.2 Human Pose and ShapePublicationTitleHighlight3DV 20243d pose estimation of two interacting hands from a monocular event cameraHand PoseCVPRW 2023MoveEnet: Online High-Frequency Human Pose Estimation With an Event CameraHuman PoseArxiv 2022Efficient Human Pose Estimation via 3D Event Point CloudArxiv 2022A Temporal Densely Connected Recurrent Network for Event-based Human Pose EstimationICCV 2021EventHands: real-time neural 3D hand pose estimation from an event streamHand PoseCVPR 2021Lifting Monocular Events to 3D Human PosesICCV 2021EventHPE: Event-based 3D Human Pose and Shape EstimationHuman PoseCVPR 2020EventCap: Monocular 3D Capture of High-Speed Human Motions Using an Event CameraHuman PoseWACV 2019Space-Time Event Clouds for Gesture Recognition: From RGB Cameras to Event CamerasHand PoseCVPR 2019DHP19: Dynamic Vision Sensor 3D Human Pose DatasetArxiv 2019EventGAN: Leveraging Large Scale Image Datasets for Event CamerasICCV 2021EventHPE: Event-Based 3D Human Pose and Shape Estimation4.3 Body and Eye TrackingPublicationTitleHighlightObject tracking on event cameras with offline–online learningReal-Time Face & Eye Tracking and Blink Detection using Event CamerasECCV 2020Stereo Event-based Particle Tracking Velocimetry for 3D Fluid Flow ReconstructionISFV 2014Large-scale Particle Tracking with Dynamic Vision SensorsT-CG 2021Event Based, Near-Eye Gaze Tracking Beyond 10,000HzDataset4.4 FacePublicationTitleHighlightSensor 2020Face pose alignment with event cameras4.5 CompressionPublicationTitleHighlightT-SPL 2020Lossless Compression of Event Camera Frames4.4 SAIPublicationTitleHighlightCVPR 2021Event-Based Synthetic Aperture Imaging With a Hybrid Network5 Robotic Vision5.1 Object Detection and TrackingThis section focuses on event-based detection/tracking tasks for Robotics implementation.PublicationTitleHighlightNeurIPS 2024EV-Eye: Rethinking High-frequency Eye Tracking through the Lenses of Event CamerasDLCVPR 2024Event Stream-based Visual Object Tracking: A High-Resolution Benchmark Dataset and A Novel BaselineDLCVPRW 2024A Lightweight Spatiotemporal Network for Online Eye Tracking with

What is the free 3D modeling/posing program that adult Renpy

Linux Video Effects SDKAugmented Reality SDK(Windows/Linux: 0.8.2)- Enable real-time 3D tracking of a person’s face using a standard web camera. Create unique AR effects such as overlaying 3D content on a face, driving 3D characters and virtual interactions in real time. Note: The Linux version of the Augmented Reality SDK is currently only available in the Early Access Program.Key FeaturesFace Tracking Face Landmark tracking Face Mesh Body Pose EstimationEye ContactFace Expression EstimationLatest Release Face Expression Estimation6DOF head pose now availableExpression estimation model updatedNew face model for visualization with updated blendshapes, and face area partitioningEye ContactPerformance improvements via CUDA graph functionalityOperating SystemsWindows 10, Windows 11 64-bit, Ubuntu 18.04, Ubuntu 20.04, CentOS 7Supported HardwareWindows SDK: NVIDIA GeForce RTX 20XX and 30XX Series, Quadro RTX 3000, TITAN RTX, or higher (any NVIDIA GPUs with Tensor Cores)Server SDK: V100, T4, A10, A30, A100 (with MIG support)Support for Ada-generation GPUs for Windows SDKsSoftware DependenciesWindows SDK: NVIDIA Display Driver 511.65+ or more recent, CMake 3.12+Server SDKs (Linux): CUDA 11.8.0, TRT 8.5.1.7, CuDNN 8.6.0.163, CMake 3.12+, NVIDIA Display Driver 520.61 or laterWindows AR SDK and Linux AR SDK (early access program)Getting started with MaxineProcedureFollow the resource cards for specifics on using each of the SDKs. SDK-specific programming guides are available inside Audio Effects SDK, Video Effects SDK, and Augmented Reality SDK Program Guides. You can also find them in the documentation which is available here.LicenseThe NVIDIA Maxine license agreement is contained in the SDK download packages. Please refer to the SDK packages for the SDK-specific licenses.Ethical AINVIDIA’s platforms and application frameworks enable developers to build a wide array of AI applications. Please consider potential algorithmic bias when choosing or creating the models being deployed. Work with the model’s developer to ensure:The model meets the requirements for the relevant industry and use caseThe necessary instruction and documentation are provided to understand error rates, confidence intervals, and resultsThe model is being used under the conditions and in the manner intended

Easy Pose - 3D pose making app

Pose并warp到canonical空间中用PIFU估计Occupancy； • 优势：可直接驱动，生成纹理质量较高； • 问题：过度依赖扫描3D人体真值来训练网络；需要非常准确的Pose估计做先验；较难处理复杂形变如长发和裙子； + 带衣服褶皱 + 带纹理 - 不能直接驱动 • PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization. In ICCV, 2019. • PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization. In CVPR, 2020. • SiCloPe: Silhouette-Based Clothed People. In CVPR, 2019. • PaMIR: Parametric Model-Conditioned Implicit Representation for Image-based Human Reconstruction. In TPAMI, 2020. • Reconstructing NBA Players. In ECCV, 2020. • 带衣服人体表示：Occupancy + RGB； • 思路1：训练网络提取空间点投影到图像位置的特征，并结合该点位置预测其Occupancy值和RGB值； • 优势：适用于任意pose，可建模复杂外观如长发裙子 • 问题：过度依赖扫描3D人体真值来训练网络；后期需要注册SMPL才能进行驱动；纹理质量并不是很高； + 带衣服褶皱 - 不带纹理 - 不能直接驱动 • BodyNet: Volumetric Inference of 3D Human Body Shapes. In ECCV, 2018. • DeepHuman: 3D Human Reconstruction From a Single Image. In ICCV, 2019. • 带衣服人体表示：voxel grid occupancy； • 思路1：预测voxel grid每个格子是否在body内部； • 优势：适用于任意pose，可建模复杂外观如长发裙子 • 问题：需要另外估纹理；分辨率较低；过度依赖扫描3D人体真值来训练网络；后期需要注册SMPL才能进行驱动； 多视角RGB图像 + 带衣服褶皱 + 带纹理 - 不能直接驱动 • Deep Volumetric Video From Very Sparse Multi-View Performance Capture. In ECCV, 2018. • PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization. In ICCV, 2019. • PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization. In CVPR, 2020. • 带衣服人体表示：Occupancy + RGB； • 思路: 多视角PIFU； • 优势：多视角信息预测更准确；适用于任意pose；可建模复杂外观如长发和裙子； • 问题：多视角数据较难采集，过度依赖扫描3D人体真值来训练网络；后期需要注册SMPL才能进行驱动；纹理质量并不是很高； 单张RGBD图像 + 带衣服褶皱 + 带纹理 - 不能直接驱动 • NormalGAN: Learning Detailed 3D Human from a Single RGB-D Image. In ECCV, 2020. • 带衣服人体表示：3D point cloud + triangulation； • 思路: GAN网络生成front-view和back-view 的depth和color，再用triangulation得到mesh； • 优势：适用于任意pose；可建模复杂外观如长发和裙子； • 问题：过度依赖扫描3D人体真值来训练网络；后期需要注册SMPL才能进行驱动；纹理质量并不是很高； RGB视频输入 + 带衣服褶皱 + 带纹理 + 能直接驱动 • Video Based Reconstruction of 3D People Models. In CVPR, 2018. • Detailed Human Avatars from Monocular Video. In 3DV, 2018. • Learning to Reconstruct People in Clothing from a Single RGB Camera.. Poser is a 3D computer graphics program for posing, animating, and rendering

Posing 3D drawing figures and 3D

Pose and shape via model-fitting in the loop. In ICCV, 2019. • I2L-MeshNet: Image-to-Lixel Prediction Network for Accurate 3D Human Pose and Mesh Estimation from a Single RGB Image. In ECCV, 2020. • Learning 3D Human Shape and Pose from Dense Body Parts. In TPAMI, 2020. • ExPose: Monocular Expressive Body Regression through Body-Driven Attention. In ECCV, 2020. • Hierarchical Kinematic Human Mesh Recovery. In ECCV, 2020. • Pose2Mesh: Graph Convolutional Network for 3D Human Pose and Mesh Recovery from a 2D Human Pose. In ECCV, 2020. • 主要思路：估计SMPL参数，加入2D keypoint loss，adversarial loss，silhouette loss等；有3D真值时可以加入SMPL参数真值、Mesh真值、3D joint真值约束；融合regression-based 和 optimization-based方法协作提升；从估计SMPL估计更精细的SMPL-X，对手部和头部强化处理； • 目前挑战：现实场景缺乏真值数据，如何产生有用的监督信号或pseudo ground-truth来帮助训练；合成数据有真值但存在domain gap，如何有效利用合成数据来帮助真实场景训练；目前很多方法估计结果在人体深度、肢体末端如手部和脚部还存在偏差，对复杂姿势估计结果仍不够准确； 动态视频 • Learning 3D Human Dynamics from Video. In CVPR, 2019. • Monocular Total Capture: Posing Face, Body, and Hands in the Wild. In CVPR, 2019. • Human Mesh Recovery from Monocular Images via a Skeleton-disentangled Representation. In ICCV, 2019. • VIBE: Video Inference for Human Body Pose and Shape Estimation. In CVPR, 2020. • PoseNet3D: Learning Temporally Consistent 3D Human Pose via Knowledge Distillation. In CVPR, 2020. • Appearance Consensus Driven Self-Supervised Human Mesh Recovery. In ECCV, 2020. • 主要思路：估计单帧SMPL参数基础上加入帧间连续性和稳定性约束；帧间联合优化；appearance一致性约束； • 目前挑战：帧间连续性和稳定性约束会对动作产生平滑效果，导致每一帧都不是很准确；估计出来的结果仍会存在漂浮、抖动、滑步等问题； 3D人体重建近年来与3D人体重建相关的工作很多，按照上述3D表示形式可分为基于Voxel表示、基于Mesh表示和基于Implicit function表示；按照输入形式可分为：基于单张图像、多视角图像和基于视频输入，这些输入都可以带有深度信息或无深度信息；按照重建效果可以分为带纹理重建和不带纹理重建，能直接驱动和不能直接驱动等等。 输入要求重建效果代表工作基本原理及评价 单张RGB图像 + 带衣服褶皱 + 带纹理 + 能直接驱动 • 360-Degree Textures of People in Clothing from a Single Image. In 3DV, 2019. • Tex2Shape: Detailed Full Human Body Geometry From a Single Image. In ICCV, 2019. • ARCH: Animatable Reconstruction of Clothed Humans. In CVPR, 2020. • 3D Human Avatar Digitization from a Single Image. In VRCAI, 2019. • 带衣服人体表示：SMPL+Deformation+Texture； • 思路1：估计3D pose采样部分纹理，再用GAN网络生成完整纹理和displacement； • 思路2：估计3D

What 3d model program can I use for posing references?

Vancouver, BC, Canada, 17–24 June 2023; pp. 12965–12976. [Google Scholar]Yi, H.; Liang, H.; Liu, Y.; Cao, Q.; Wen, Y.; Bolkart, T.; Tao, D.; Black, M.J. Generating holistic 3D human motion from speech. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada, 17–24 June 2023; pp. 469–480. [Google Scholar]Bhatnagar, B.L.; Tiwari, G.; Theobalt, C.; Pons-Moll, G. Multi-garment net: Learning to dress 3D people from images. In Proceedings of the IEEE/CVF International Conference on Computer Vision, Seoul, Republic of Korea, 27 October–2 November 2019; pp. 5420–5430. [Google Scholar]Santesteban, I.; Otaduy, M.A.; Casas, D. Learning-based animation of clothing for virtual try-on. Comput. Graph. Forum 2019, 38, 355–366. [Google Scholar] [CrossRef]Kolotouros, N.; Pavlakos, G.; Black, M.J.; Daniilidis, K. Learning to reconstruct 3D human pose and shape via model-fitting in the loop. In Proceedings of the IEEE/CVF International Conference on Computer Vision, Seoul, Republic of Korea, 27 October–2 November 2019; pp. 2252–2261. [Google Scholar]Pavlakos, G.; Choutas, V.; Ghorbani, N.; Bolkart, T.; Osman, A.A.; Tzionas, D.; Black, M.J. Expressive body capture: 3D hands, face, and body from a single image. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA, 15–20 June 2019; pp. 10975–10985. [Google Scholar]Chun, S.; Park, S.; Chang, J.Y. Learnable human mesh triangulation for 3D human pose and shape estimation. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA, 2–7 January 2023; pp. 2850–2859. [Google Scholar]Sayo, A.; Thomas, D.; Kawasaki, H.; Nakashima, Y.; Ikeuchi, K. PoseRN: A 2D pose refinement network for bias-free multi-view 3D human pose estimation. In Proceedings of the 2021 IEEE International Conference on Image Processing (ICIP), Anchorage, AK, USA, 19–22 September 2021; pp. 3233–3237. [Google Scholar]Li, Z.; Heyden, A.; Oskarsson, M. A novel joint points and silhouette-based method to estimate 3D

Simple 3d posing programs that have light sources attached I

3D Human 相关研究总结（Body、Pose、Reconstruction、Cloth、Animation）前言本文简要介绍与3D数字人相关的研究，包括常用3D表示、常用3D人体模型、3D人体姿态估计，带衣服3D人体重建，3D衣服建模，以及人体动作驱动等。常用3D表示目前3D 学习中，物体或场景的表示包括显式表示与隐式表示两种，主流的显式表示包括基于voxel、基于point cloud、和基于polygon mesh三种，隐式表示包括基于Occupancy Function[1]、和基于Signed Distance Functions[2]两种。下表简要总结了各种表示方法的原理及其相应优缺点。 表示方法VoxelPoint CloudPolygon MeshOccupancy FunctionSigned Distance Function 表示图像 表示原理 体素用规则的立方体表示3D物体，体素是数据在三维空间中的最小分割单位，类似于2D图像中的像素点云将多面体表示为三维空间中点的集合，一般用激光雷达或深度相机扫描后得到点云数据多边形网格将多面体表示为顶点与面片的集合，包含了物体表面的拓扑信息occupancy function 将物体表示为一个占有函数，即空间中每个点是否在表面上SDF 将物体表示为符号距离函数，即空间中每个点距离表面的距离 优缺点 + 规则表示，容易送入网络学习 + 可以处理任意拓扑结构 - 随着分辨率增加，内存呈立方级增长- 物体表示不够精细- 纹理不友好+ 容易获取+ 可以处理任意拓扑结构- 缺少点与点之间连接关系- 物体表示不够精细- 纹理不友好+ 高质量描述3D几何结构+ 内存占有较少 + 纹理友好- 不同物体类别需要不同的 mesh 模版- 网络较难学习+ 可以精细建模细节，理论上分辨率无穷+ 内存占有少 + 网络较易学习 - 需后处理得到显式几何结构+ 可以精细建模细节，理论上分辨率无穷+ 内存占有少 + 网络较易学习 - 需后处理得到显式几何结构 [1] Occupancy Networks: Learning 3D Reconstruction in Function Space. In CVPR, 2019.[2] DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation. In CVPR, 2019.常用3D人体模型目前常用的人体参数化表示模型为德国马克斯•普朗克研究所提出的SMPL[3]，该模型采用6890个顶点（vertices）, 和13776 个面片（faces）定义人体 template mesh，并采用10维参数向量控制人体shape，24个关节点旋转参数控制人体pose，其中每个关节点旋转参数采用3维向量来表示该关节相对其父关节分别沿着x,y,z轴的旋转角。该研究所在CVPR 2019上提出 SMPL-X [4]，采用了更多顶点来精细建模人体，并加入了面部表情和手部姿态的参数化控制。这两篇工作给出了规范的、通用的、可以与工业3D软件如Maya和Unity相通的人体参数化表示，并提出了一套简单有效的蒙皮策略，使得人体表面的顶点跟随关节旋转运动时不会产生明显瑕疵。近年来也有不少改进的人体模型，如SoftSMPL[5]，STAR[6]，BLSM[7]，GHUM[8]等。 人体模型SMPLSMPL-X 基本表示• mesh表示：6890 vertices, 13776 faces • pose控制：24个关节点，24*3维旋转向量• shape控制：10维向量• mesh表示：10475 vertices, 20908 faces • pose控制：身体54个关节点，75维PCA• 手部控制：24维PCA• 表情控制：10维向量• shape控制：10维向量 示意图 [3] SMPL: A Skinned Multi-Person Linear Model. In SIGGRAPH Asia, 2015.[4] Expressive Body Capture: 3D Hands, Face, and Body from a Single Image. In CVPR, 2019.[5] SoftSMPL: Data-driven Modeling of Nonlinear Soft-tissue Dynamics for Parametric Humans. In Eurographics, 2020.[6] STAR: Sparse Trained Articulated Human Body Regressor. ECCV, 2020.[7] BLSM: A Bone-Level Skinned Model of the Human Mesh. ECCV, 2020.[8] GHUM & GHUML: Generative 3D Human Shape and Articulated Pose Models. CVPR (Oral), 2020.3D人体姿态估计3D人体姿态估计是指从图像、视频、或点云中估计人物目标的体型（shape）和姿态（pose），是围绕人体3D研究中的一项基本任务。3D人体姿态估计是3D人体重建的重要前提，也可以是人体动作驱动中动作的重要来源。目前很多3D姿态估计算法主要是估计场景中人体的SMPL参数。根据场景不同，可以分为针对单张图像和针对动态视频的人体3D姿态估计。下表简要总结了目前两种场景下的一些代表工作，并给出了一些简要原理介绍和评价。 场景代表工作原理及评价 单张图像 • Keep it SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image. In ECCV, 2016. • End-to-end Recovery of Human Shape and Pose. In CVPR, 2018. • Learning to Estimate 3D Human Pose and Shape from a Single Color Image. In CVPR, 2018. • Delving Deep into Hybrid Annotations for 3D Human Recovery in the Wild. In ICCV, 2019. • SPIN: Learning to reconstruct 3d human

Easy Pose - 3D pose making app - Apps

$25.95 $25.95 Compatible Figures: Genesis 2 Female, Genesis 2 Male, Genesis 8.1 Female, Genesis 8.1 Male, Genesis 8 Male, Genesis, Genesis 3 Female, Genesis 3 Male, Genesis 8 Female Compatible Software: Daz Studio 4.23 SKU:71569 Optional License Add-Ons: *Interactive License −70% $50.00 $15.00 *3D Printing License −70% $1.99 $0.60 *Unless otherwise specified, no discounts or offers will apply to License Add‑Ons. Compatible Figures: Genesis 3 Male, Genesis 8 Female, Genesis 8 Male, Genesis, Genesis 3 Female, Genesis 8.1 Female, Genesis 8.1 Male, Genesis 2 Female, Genesis 2 Male Compatible Software: Daz Studio 4.23 SKU:71569 Optional License Add-Ons: *Interactive License −70% $50.00 $15.00 *3D Printing License −70% $1.99 $0.60 *Unless otherwise specified, no discounts or offers will apply to License Add‑Ons. This product is in this bundle Details Introducing Pose Fusion Plus!Based on the power of Pose Fusions design, this product gives you the ability to easily mix and match poses, as well as save them out any way you want. Split any pose and load it onto any part of the figure. Owners of Pose Fusion will also have its interface automatically integrated inside Plus, meaning only one script is needed for usage. Pose Fusion is not a requirement in order to use the native features inside Pose Fusion Plus.You can also minimize the script or assign a shortcut key to open the script which will speed up your workflow when modifying or creating a pose.Note: Pose Fusion Plus can also be used as a Merchant Resource as long as. Poser is a 3D computer graphics program for posing, animating, and rendering In this article, we will explore the top 10 character posing software programs that are popular among animators and illustrators. 1. Daz 3D. Daz 3D is a versatile character posing

Easy Pose - 3D pose making app APK for

In CVPR, 2019. • Multi-Garment Net: Learning to Dress 3D People from Images. In ICCV, 2019. • 带衣服人体表示：SMPL+Deformation+Texture； • 思路1：多帧联合估计canonical T-pose下的SMPL+D，投影回每帧提取纹理融合； • 优势：可直接驱动；生成纹理质量较高；简单场景下效果较好； • 问题：过度依赖扫描3D人体真值来训练网络；需要较准确的Pose估计和human parsing做先验；较难处理复杂形变如长发裙子 + 带衣服褶皱 - 不带纹理 - 不能直接驱动 • MonoClothCap: Towards Temporally Coherent Clothing Capture from Monocular RGB Video. In 3DV, 2020. • 带衣服人体表示：SMPL+Deformation； • 思路：每帧估计SMPL参数并联合多帧优化得到稳定shape和每帧pose，为不同衣服建模形变参数化模型，约束Silhouette，Clothing segmentation，Photometric，normal等信息一致 • 优势：无需3D真值；可以建模较为细致的衣服形变； • 问题：依赖较准确的pose和segmentation估计；只能处理部分衣服类型； RGBD视频输入 + 带衣服 + 带纹理 + 也许能直接驱动 • Robust 3D Self-portraits in Seconds. In CVPR, 2020. • TexMesh: Reconstructing Detailed Human Texture and Geometry from RGB-D Video. In ECCV, 2020. • 带衣服人体表示：Occupancy+RGB； • 思路1：RGBD版PIFU生成每帧先验，TSDF (truncated signed distance function) 分为inner model和surface layer，PIFusion做double layer-based non-rigid tracking，多帧联合微调优化得到3D portrait； • 优势：建模较精细，可以处理较大形变如长发和裙子；不需要扫描真值； • 问题：流程略复杂；纹理质量一般； Depth视频输入 + 带衣服褶皱 - 不带纹理 + 也许能直接驱动 • DoubleFusion: Real-time Capture of Human Performances with Inner Body Shapes from a Single Depth Sensor. In CVPR, 2018. • 带衣服人体表示：outer layer + inner layer(SMPL) • 思路：joint motion tracking, geometric fusion and volumetric shape-pose optimization • 优势：建模较精细；速度快，可以实时； • 问题：无纹理； 3D衣服建模在3D人体重建任务中，衣服一般是用与template mesh每个顶点绑定的Deformation来表示，但这种表示并不能精细建模衣服的纹理褶皱等细节，在人物模型运动起来时也会很不自然。因此近年来也有一部分工作将3D衣服建模与深度神经网络结合，旨在不同shape和pose情况下，准确逼真地模拟、预测人体衣服的形变。 代表工作基本原理简要评价 • Physics-Inspired Garment Recovery from a Single-View Image. In TOG, 2018. • 思路：衣服分割+衣服特征估计(尺码，布料，褶皱)+人体mesh估计，材质-姿态联合优化+衣物仿真； • 优势：衣服和人体参数化表示较规范；引入物理、统计、几何先验； • 问题：衣服特征估计受光照和图像质量影响较大，受限于garment模版的丰富程度；需要后期通过衣物仿真联合优化来调整效果； • DeepWrinkles: Accurate and Realistic Clothing Modeling. In ECCV, 2018. • 思路：统计模型学习衣服在某pose和shape下的大致效果，GAN模型生成更细致的褶皱； • 优势：用GAN可以生成逼真细致的褶皱； • 问题：依赖4D扫描动作序列真值；需提前做好衣服注册； • Multi-Garment Net: Learning to Dress 3D People from Images. In ICCV, 2019. • 思路：human parsing分割衣服并预测类别，估计衣服PCA参数和细节Displacement； • 优势：明确3D scan segmentation和Garment registration的pipeline；引入Human parsing可以得到更准确的衣服类别； • 问题：过度依赖3D真值训练；PCA参数表示的准确性依赖dataset大小； • Learning-Based Animation of Clothing for Virtual Try-On. In EUROGRAPHICS, 2019. • 思路：衣服仿真生成真值帮助网络训练，基于shape学习衣服模版变形，基于pose和shape学习动态褶皱， • 优势：衣物仿真可以得到任意pose下的大量真值数据； • 问题：与现实数据差距较大；依赖衣物模版的丰富程度；直接学习defromation不够稳定，容易穿模需后处理； • TailorNet: Predicting Clothing in 3D as a Function of Human Pose, Shape and Garment Style. In CVPR, 2020. • 思路：将衣服形变分为高频和低频，低频部分用网络估计大致形变，高频部分估计多个特定style-shape模型，每个模型负责估计特定形变及加权权重； • 优势：可以得到较为细致的衣服褶皱；提出合成数据集，仿真20件衣服，1782个pose和9种shape；

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And physique modification simply can’t be matched. I draw Manga, Anime, and lifelike Characters in my comicbooks, and this app truly excels at nailing the specific pose in the scenes I draw. I have purchased Manga Poser, Pose 3D, and a few others, yet I come back to this.It has the Best and most realistic and detailed muscle structure for both male and female.Minor remarks: add more save slots. And if you are a serious artist, do not depend on this solely for your artwork. I use it as a cheat to speed up production, but you MUST have basic knowledge of drawing and anatomy in general if you are more than just a casual artist. The problem with many Japanese 3D models is they tend to large hands and feet and really squared shoulders. You MUST learn properly catch the Essence of the specific pose, then add your own drawing experience to perfectly refine weird elements that don’t transfer well from 3D to hand drawn sketch. Besides that, this app is the best pose reference guide I’ve ever purchased and YES, even improved my own artwork (especially when drawing hands)BUY IT. And devs, come on already give us another update, lol. Peace. Information Seller shawn ogle Size 318.7 MB Category Reference Compatibility Requires iOS 11.0 or later. Compatible with iPhone, iPad, and iPod touch. Age Rating 12+ Infrequent/Mild Sexual Content and Nudity Copyright © Shawn Ogle 2017 Price App Bundle $9.99 Developer Website Privacy Policy Developer Website Privacy Policy. Poser is a 3D computer graphics program for posing, animating, and rendering

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52.92, IOUvox: 57.04, IOUprojFV: 86.97, IOUprojSV: 66.29, IOUpartvox: 0.00, LR: 1e-03, DataLoadingTime 0.570Epoch: [1][4/2000] Time: 1.678, Err: 0.771 PCK: 50.00, PixelAcc: 42.95, IOU: 36.04, RMSE: 0.00, PE3Dvol: 99.04, IOUvox: 52.74, IOUprojFV: 83.87, IOUprojSV: 64.07, IOUpartvox: 0.00, LR: 1e-03, DataLoadingTime 0.1012D pose (PCK), 2D body part segmentation (PixelAcc, IOU), depth (RMSE), 3D pose (PE3Dvol), voxel prediction (IOUvox), side-view and front-view re-projection (IOUprojFV, IOUprojSV) performances are reported at each iteration.The final network is a result of a multi-stage training.SubNet1 - model_segm_cmu.t7. RGB -> Segmobtained from here and the first two stacks are extractedSubNet2 - model_joints2D.t7. RGB -> Joints2Dtrained on MPII with 8 stacks, and the first two stacks are extractedSubNet3 - model_joints3D_cmu.t7. RGB + Segm + Joints2D -> Joints3Dtrained from scratch with 2 stacks using predicted segmentation (SubNet1) and 2D pose (SubNet2)SubNet4 - model_voxels_cmu.t7. RGB + Segm + Joints2D + Joints3D -> Voxelstrained from scratch with 2 stacks using predicted segmentation (SubNet1), 2D pose (SubNet2), and 3D pose (SubNet3)SubNet5 - model_voxels_FVSV_cmu.t7. RGB + Segm + Joints2D + Joints3D -> Voxels + FV + SVpre-trained from SubNet4 with the additional losses on re-projectionBodyNet - model_bodynet_cmu.t7. RGB -> Segm + Joints2D + Joints3D + Voxels + FV + SVa combination of SubNet1, SubNet2, SubNet3, SubNet4, and SubNet5fine-tuned end-to-end with multi-task lossesNote that the performance with 8 stacks is generally better, but we preferred to reduce the complexity with the cost of a little performance.Above recipe is used for the SURREAL dataset. For the UP dataset, we first fine-tuned the SubNet1 model_segm_UP.t7 (SubNet1_UP). Then, we fine-tuned SubNet3 model_joints3D_UP.t7 (SubNet3_UP) using SubNet1_UP and SubNet2. Finally, we fine-tuned SubNet5 model_voxels_FVSV_UP.t7 (SubNet5_UP) using SubNet1_UP, SubNet2, and SubNet3_UP. All these are fine-tuned end-to-end to obtain model_bodynet_UP.t7. The model used in the paper for experimenting with the manual segmentations is also provided model_voxels_FVSV_UP_manualsegm.t7.Part VoxelsWe use the script models/init_partvoxels.lua to copy the last layer weights 7 times (6 body parts + 1 background) to initialize the part voxels model (models/t7/init_partvoxels.t7). After training this model without re-projection losses, we fine-tune it with re-projection loss. model_partvoxels_cmu.t7 is the best model obtained. With end-to-end fine-tuning, we had divergence problems and did not put too much effort to make it work. Note that this model is preliminary and needs improvement.MiscA few functionalities of the code are not used in the paper; however, still provided. These include training 3D pose and voxels networks using ground truth (GT) segmentation/2D pose/3D pose inputs, as well as mixing the predicted and GT inputs at each batch. This is achieved by setting the mix option to true. The results of only using predicted inputs are often comparable to using a mix, therefore we always used only predictions. Predictions are passed as input using the applyHG option, which is not very efficient.3. TestingUse the demo script to apply the provided models on sample images.You can also use demo/demo.m Matlab script to produce visualizations.4. Fitting SMPL modelFitting scripts for SURREAL (fitting/fit_surreal.py) and UP (fitting/fit_up.py) datasets are provided with sample experiment outputs. The scripts use the optimization functions from tools/smpl_utils.py.CitationIf you use this