# TightCap: 3D Human Shape Capture with Clothing Tightness Field

arXiv: https://arxiv.org/abs/1904.02601 · HTML: https://mlsanity.com/paper/1904.02601/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Apr 2019.

## Built on by (most cited first, 25 of 25)

- [Capturing and Animation of Body and Clothing from Monocular Video](https://mlsanity.com/paper/2210.01868/) (2022)
- [End-to-End 3D Dense Captioning with Vote2Cap-DETR](https://mlsanity.com/paper/2301.02508/) (2023)
- [Learning Disentangled Avatars with Hybrid 3D Representations](https://mlsanity.com/paper/2309.06441/) (2023)
- [DiffAvatar: Simulation-Ready Garment Optimization with Differentiable Simulation](https://mlsanity.com/paper/2311.12194/) (2023)
- [Neural Free-Viewpoint Performance Rendering under Complex Human-object Interactions](https://mlsanity.com/paper/2108.00362/) (2021)
- [HumanLiff: Layer-wise 3D Human Generation with Diffusion Model](https://mlsanity.com/paper/2308.09712/) (2023)
- [GALA: Generating Animatable Layered Assets from a Single Scan](https://mlsanity.com/paper/2401.12979/) (2024)
- [CaPhy: Capturing Physical Properties for Animatable Human Avatars](https://mlsanity.com/paper/2308.05925/) (2023)
- [TELA: Text to Layer-wise 3D Clothed Human Generation](https://mlsanity.com/paper/2404.16748/) (2024)
- [Dressing Avatars: Deep Photorealistic Appearance for Physically Simulated Clothing](https://mlsanity.com/paper/2206.15470/) (2022)
- [ReLoo: Reconstructing Humans Dressed in Loose Garments from Monocular Video in the Wild](https://mlsanity.com/paper/2409.15269/) (2024)
- [Disco4D: Disentangled 4D Human Generation and Animation from a Single Image](https://mlsanity.com/paper/2409.17280/) (2024)
- [DC-Scene: Data-Centric Learning for 3D Scene Understanding](https://mlsanity.com/paper/2505.15232/) (2025)
- [3D CoCa: Contrastive Learners are 3D Captioners](https://mlsanity.com/paper/2504.09518/) (2025)
- [Human Image Generation: A Comprehensive Survey](https://mlsanity.com/paper/2212.08896/) (2022)
- [ETCH: Generalizing Body Fitting to Clothed Humans via Equivariant Tightness](https://mlsanity.com/paper/2503.10624/) (2025)
- [Disentangled Clothed Avatar Generation with Layered Representation](https://mlsanity.com/paper/2501.04631/) (2025)
- [High-Quality Animatable Dynamic Garment Reconstruction from Monocular Videos](https://mlsanity.com/paper/2311.01214/) (2023)
- [Open-Vocabulary Semantic Part Segmentation of 3D Human](https://mlsanity.com/paper/2502.19782/) (2025)
- [3D CoCa v2: Contrastive Learners with Test-Time Search for Generalizable Spatial Intelligence](https://mlsanity.com/paper/2601.06496/) (2026)
- [Refining 3D Human Texture Estimation from a Single Image](https://mlsanity.com/paper/2303.03471/) (2023)
- [ETCH-X: Robustify Expressive Body Fitting to Clothed Humans with Composable Datasets](https://mlsanity.com/paper/2604.08548/) (2026)
- [Ultron: Enabling Temporal Geometry Compression of 3D Mesh Sequences using Temporal Correspondence and Mesh Deformation](https://mlsanity.com/paper/2409.05151/) (2024)
- [PhyDeformer: High-Quality Non-Rigid Garment Registration with Physics-Awareness](https://mlsanity.com/paper/2501.10455/) (2025)
- [OpenHuman4D: Open-Vocabulary 4D Human Parsing](https://mlsanity.com/paper/2507.09880/) (2025)

## Abstract (arXiv)

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 2D geometry image. Based on this 2D representation, we propose a novel framework to predicted clothing tightness via a novel tightness formulation, as well as an effective optimization scheme to further reconstruct multi-layer human shape and garments under various clothing categories and human postures. We further propose a new clothing tightness dataset (CTD) of human scans with a large variety of clothing styles, poses and corresponding ground-truth human shapes to stimulate further research. Extensive experiments demonstrate the effectiveness of our TightCap to achieve high-quality human shape and dressed garments reconstruction, as well as the further applications for clothing segmentation, retargeting and animation.