Deep Fashion3D: A Dataset and Benchmark for 3D Garment Reconstruction from Single Images
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Shenzhen Research Institute of Big Data（深圳大数据研究院）；Tencent America（腾讯美国）；University of Science and Technology of China（中国科学技术大学）；Zhejiang University（浙江大学）；Xidian University（西安电子科技大学）
Heming Zhu, Yu Cao, Hang Jin, Weikai Chen, Dong Du, Zhangye Wang, Shuguang Cui, Xiaoguang Han
High-fidelity clothing reconstruction is the key to achieving photorealism in a wide range of applications including human digitization, virtual try-on, etc. Recent advances in learning-based approaches have accomplished unprecedented accuracy in recovering unclothed human shape and pose from single images, thanks to the availability of powerful statistical models, e.g. SMPL, learned from a large number of body scans. In contrast, modeling and recovering clothed human and 3D garments remains notoriously difficult, mostly due to the lack of large-scale clothing models available for the research community. We propose to fill this gap by introducing Deep Fashion3D, the largest collection to date of 3D garment models, with the goal of establishing a novel benchmark and dataset for the evaluation of image-based garment reconstruction systems. Deep Fashion3D contains 2078 models reconstructed from real garments, which covers 10 different categories and 563 garment instances. It provides rich annotations including 3D feature lines, 3D body pose and the corresponded multi-view real images. In addition, each garment is randomly posed to enhance the variety of real clothing deformations. To demonstrate the advantage of Deep Fashion3D, we propose a novel baseline approach for single-view garment reconstruction, which leverages the merits of both mesh and implicit representations. A novel adaptable template is proposed to enable the learning of all types of clothing in a single network. Extensive experiments have been conducted on the proposed dataset to verify its significance and usefulness. We will make Deep Fashion3D publicly available upon publication.
GarmentX: Autoregressive Parametric Representations for High-Fidelity 3D Garment Generation
South China University of Technology（华南理工大学）；National University of Singapore（新加坡国立大学）；LIGHTSPEED（光速（公司））；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The University of Hong Kong（香港大学）
ShanghaiTech University（上海科技大学）；University of Chinese Academy of Sciences（中国科学院大学）；Huazhong University of Science & Technology（华中科技大学）
In this paper, we present TightCap, a data-driven scheme to capture both the human shape and dressed garments accurately with only a single 3D human scan, which enables numerous applications such as virtual try-on, biometrics and body evaluation. To break the severe variations of the human poses and garments, we propose to model the clothing tightness - the displacements from the garments to the human shape implicitly in the global UV texturing domain. To this end, we utilize an enhanced statistical human template and an effective multi-stage alignment scheme to map the 3D scan into a hybrid 2…
Graduate School of Culture Technology（文化技术研究生院）；KAIST（韩国科学技术院）
Garments are ubiquitous in both real and many of the virtual worlds. They are highly deformable objects, exhibit an immense variety of designs and shapes, and yet, most garments are created from a set of regularly shaped flat pieces. Exploration of garment structure presents a peculiar case for an object structure estimation task and might prove useful for downstream tasks of neural 3D garment modeling and reconstruction by providing strong prior on garment shapes. To facilitate research in these directions, we propose a method for generating large synthetic datasets of 3D garment designs and …
Being able to forecast the popularity of new garment designs is very important in an industry as fast paced as fashion, both in terms of profitability and reducing the problem of unsold inventory. Here, we attempt to address this task in order to provide informative forecasts to fashion designers within a virtual reality designer application that will allow them to fine tune their creations based on current consumer preferences within an interactive and immersive environment. To achieve this we have to deal with the following central challenges: (1) the proposed method should not hinder the cr…
Nanyang Technological University（南洋理工大学）；SenseTime Research（商汤科技研究院）；Shanghai AI Laboratory（上海人工智能实验室）
Learning to reconstruct 3D garments is important for dressing 3D human bodies of different shapes in different poses. Previous works typically rely on 2D images as input, which however suffer from the scale and pose ambiguities. To circumvent the problems caused by 2D images, we propose a principled framework, Garment4D, that uses 3D point cloud sequences of dressed humans for garment reconstruction. Garment4D has three dedicated steps: sequential garments registration, canonical garment estimation, and posed garment reconstruction. The main challenges are two-fold: 1) effective 3D feature lea…
Fueled by the power of deep learning techniques and implicit shape learning, recent advances in single-image human digitalization have reached unprecedented accuracy and could recover fine-grained surface details such as garment wrinkles. However, a common problem for the implicit-based methods is that they cannot produce separated and topology-consistent mesh for each garment piece, which is crucial for the current 3D content creation pipeline. To address this issue, we proposed a novel geometry inference framework ReEF that reconstructs topology-consistent layered garment mesh by registering…
Inception Institute of Artificial Intelligence（人工智能初学者研究所）；ReLER, University of Technology Sydney（悉尼科技大学ReLER）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
While single-view 3D reconstruction has made significant progress benefiting from deep shape representations in recent years, garment reconstruction is still not solved well due to open surfaces, diverse topologies and complex geometric details. In this paper, we propose a novel learnable Anchored Unsigned Distance Function (AnchorUDF) representation for 3D garment reconstruction from a single image. AnchorUDF represents 3D shapes by predicting unsigned distance fields (UDFs) to enable open garment surface modeling at arbitrary resolution. To capture diverse garment topologies, AnchorUDF not o…
RefTon: Reference person shot assist virtual Try-on
The University of Tokyo（东京大学）；AI Research（360AI研究院）；University of California, San Diego（加州大学圣地亚哥分校）；The Hong Kong University of Science and Technology（香港科学与技术大学）
Garment representation, editing and animation are challenging topics in the area of computer vision and graphics. It remains difficult for existing garment representations to achieve smooth and plausible transitions between different shapes and topologies. In this work, we introduce, DeepCloth, a unified framework for garment representation, reconstruction, animation and editing. Our unified framework contains 3 components: First, we represent the garment geometry with a "topology-aware UV-position map", which allows for the unified description of various garments with different shapes and top…
Spatio-Temporal Garment Reconstruction Using Diffusion Mapping via Pattern Coordinates
CVLab, École Polytechnique Fédérale de Lausanne (EPFL)（瑞士联邦理工学院（EPFL）计算机视觉实验室）；Mohamed bin Zayed University of Artificial Intelligence（马尔代夫人工智能大学）；Department of Computer Science and Engineering, Southern University of Science and Technology（南方科技大学计算机科学与工程系）；Pinscreen and Mohamed bin Zayed University of Artificial Intelligence（Pinscreen和马尔代夫人工智能大学）
Dressed people reconstruction from images is a popular task with promising applications in the creative media and game industry. However, most existing methods reconstruct the human body and garments as a whole with the supervision of 3D models, which hinders the downstream interaction tasks and requires hard-to-obtain data. To address these issues, we propose an unsupervised separated 3D garments and human reconstruction model (USR), which reconstructs the human body and authentic textured clothes in layers without 3D models. More specifically, our method proposes a generalized surface-aware …
ClothesNet: An Information-Rich 3D Garment Model Repository with Simulated Clothes Environment
Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Beihang University（北京航空航天大学）；Shanghai Jiao Tong University（上海交通大学）；Nanjing University（南京大学）；National University of Singapore（新加坡国立大学）；Xi’an Jiaotong University（西安交通大学）
DAMA: Disentangled Body-Anchored Gaussians for Controllable Multi-Layered Avatars
University of Tübingen（图宾根大学）；Tübingen AI Center（图宾根人工智能中心）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；Max Planck Institute for Informatics（马克斯·普朗克信息研究所）；Zuse School ELIZA（祖斯学校ELIZA）
Existing approaches for 3D garment reconstruction either assume a predefined template for the garment geometry (restricting them to fixed clothing styles) or yield vertex colored meshes (lacking high-frequency textural details). Our novel framework co-learns geometric and semantic information of garment surface from the input monocular image for template-free textured 3D garment digitization. More specifically, we propose to extend PeeledHuman representation to predict the pixel-aligned, layered depth and semantic maps to extract 3D garments. The layered representation is further exploited to …
TryOffDiff: Virtual-Try-Off via High-Fidelity Garment Reconstruction using Diffusion Models
Machine Learning Group, CITEC Bielefeld University Germany（比勒菲尔德大学机器学习小组、CITEC）；Institute of Mathematics Technical University Berlin Germany（柏林技术大学数学研究所）
University of Minnesota（明尼苏达大学）；Adobe Research（奥多比研究院）
Clothes undergo complex geometric deformations, which lead to appearance changes. To edit human videos in a physically plausible way, a texture map must take into account not only the garment transformation induced by the body movements and clothes fitting, but also its 3D fine-grained surface geometry. This poses, however, a new challenge of 3D reconstruction of dynamic clothes from an image or a video. In this paper, we show that it is possible to edit dressed human images and videos without 3D reconstruction. We estimate a geometry aware texture map between the garment region in an image an…
Stitched Embeddings: A Unified Latent Space for 3D Garments and 2D Patterns
University of Tübingen（图宾根大学）；Tübingen AI Center（图宾根人工智能中心）；Technical University of Munich（慕尼黑工业大学）；Munich Center for Machine Learning（慕尼黑机器学习中心）；Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；Meta
Huawei Noah’s Ark Lab（华为诺亚方舟实验室）；Imperial College London（伦敦帝国理工学院）；University of Oxford（牛津大学）；Technical University of Munich（慕尼黑工业大学）
For visual manipulation tasks, we aim to represent image content with semantically meaningful features. However, learning implicit representations from images often lacks interpretability, especially when attributes are intertwined. We focus on the challenging task of extracting disentangled 3D attributes only from 2D image data. Specifically, we focus on human appearance and learn implicit pose, shape and garment representations of dressed humans from RGB images. Our method learns an embedding with disentangled latent representations of these three image properties and enables meaningful re-a…
This paper presents a new large multiview dataset called HUMBI for human body expressions with natural clothing. The goal of HUMBI is to facilitate modeling view-specific appearance and geometry of five primary body signals including gaze, face, hand, body, and garment from assorted people. 107 synchronized HD cameras are used to capture 772 distinctive subjects across gender, ethnicity, age, and style. With the multiview image streams, we reconstruct high fidelity body expressions using 3D mesh models, which allows representing view-specific appearance. We demonstrate that HUMBI is highly eff…
Clothing plays a fundamental role in digital humans. Current approaches to animate 3D garments are mostly based on realistic physics simulation, however, they typically suffer from two main issues: high computational run-time cost, which hinders their development; and simulation-to-real gap, which impedes the synthesis of specific real-world cloth samples. To circumvent both issues we propose PERGAMO, a data-driven approach to learn a deformable model for 3D garments from monocular images. To this end, we first introduce a novel method to reconstruct the 3D geometry of garments from a single i…
PatternGSL: A Structured Specification Language for Template-Free and Simulation-Ready 3D Garments
The University of Hong Kong（香港大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Carnegie Mellon University（卡内基梅隆大学）；LIGHTSPEED Shenzhen（LIGHTSPEED深圳）；LIGHTSPEED Los Angeles（LIGHTSPEED洛杉矶）
CORAL: A Benchmark for Structure-aware and Brain-wide Neuron Reconstruction in Light Microscopy
Tsinghua University（清华大学）；Tsinghua Laboratory of Brain and Intelligence（清华大学生物医学工程系脑与智能实验室）；IDG/McGovern Institute for Brain Research（IDG/麦戈文脑科学研究所）；School of Basic Medical Sciences（医学科学系）；Tsinghua-Peking Center for Life Sciences（清华-北京大学生命科学联合中心）
Japan Advanced Institute of Science and Technology（日本北陆先端科学技术大学院大学）
3D model reconstruction from a single image has achieved great progress with the recent deep generative models. However, the conventional reconstruction approaches with template mesh deformation and implicit fields have difficulty in reconstructing non-watertight 3D mesh models, such as garments. In contrast to image-based modeling, the sketch-based approach can help users generate 3D models to meet the design intentions from hand-drawn sketches. In this study, we propose Sketch2Cloth, a sketch-based 3D garment generation system using the unsigned distance fields from the user's sketch input. …
3D Face Reconstruction Error Decomposed: A Modular Benchmark for Fair and Fast Method Evaluation
The Children’s Hospital of Philadelphia, USA（美国费城儿童医院）；Department of Architecture and Engineering, University of Parma, Italy（意大利帕尔马大学建筑与工程系）；Department of Information Engineering and Matemathics, University of Siena, Italy（意大利锡耶纳大学信息工程与数学系）；Media Integration and Communication Center (MICC), University of Florence, Italy（意大利佛罗伦萨大学媒体整合与通信中心）；Idiap Research Institute, Switzerland（瑞士IDiap研究所）；École Polytechnique Fédérale de Lausanne (EPFL), Switzerland（瑞士洛桑联邦理工学院）；University of Pennsylvania, USA（美国宾夕法尼亚大学）
Alibaba Inc.（阿里巴巴集团）；Chinese Academy of Sciences（中国科学院）
3D face reconstruction is a fundamental task that can facilitate numerous applications such as robust facial analysis and augmented reality. It is also a challenging task due to the lack of high-quality datasets that can fuel current deep learning-based methods. However, existing datasets are limited in quantity, realisticity and diversity. To circumvent these hurdles, we introduce Pixel-Face, a large-scale, high-resolution and diverse 3D face dataset with massive annotations. Specifically, Pixel-Face contains 855 subjects aging from 18 to 80. Each subject has more than 20 samples with various…
The University of Hong Kong（香港大学）；Alibaba Group（阿里巴巴集团）；The Hong Kong Polytechnic University（香港理工大学）
High dynamic range (HDR) video reconstruction from sequences captured with alternating exposures is a very challenging problem. Existing methods often align low dynamic range (LDR) input sequence in the image space using optical flow, and then merge the aligned images to produce HDR output. However, accurate alignment and fusion in the image space are difficult due to the missing details in the over-exposed regions and noise in the under-exposed regions, resulting in unpleasing ghosting artifacts. To enable more accurate alignment and HDR fusion, we introduce a coarse-to-fine deep learning fra…
Technion – Israel Institute of Technology（以色列理工学院）；Facebook Reality Labs（Facebook现实实验室）；Carnegie Mellon University（卡内基梅隆大学）
Virtual telepresence is the future of online communication. Clothing is an essential part of a person's identity and self-expression. Yet, ground truth data of registered clothes is currently unavailable in the required resolution and accuracy for training telepresence models for realistic cloth animation. Here, we propose an end-to-end pipeline for building drivable representations for clothing. The core of our approach is a multi-view patterned cloth tracking algorithm capable of capturing deformations with high accuracy. We further rely on the high-quality data produced by our tracking meth…
