# Geometry-Aware Network for Non-Rigid Shape Prediction from a Single View

arXiv: https://arxiv.org/abs/1809.10305 · HTML: https://mlsanity.com/paper/1809.10305/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2018.

## Built on by (most cited first, 15 of 15)

- [H3D-Net: Few-Shot High-Fidelity 3D Head Reconstruction](https://mlsanity.com/paper/2107.12512/) (2021)
- [State of the Art in Dense Monocular Non-Rigid 3D Reconstruction](https://mlsanity.com/paper/2210.15664/) (2022)
- [φ-SfT: Shape-from-Template with a Physics-Based Deformation Model](https://mlsanity.com/paper/2203.11938/) (2022)
- [Reconstruction of Manipulated Garment with Guided Deformation Prior](https://mlsanity.com/paper/2405.10934/) (2024)
- [Physics-guided Shape-from-Template: Monocular Video Perception through Neural Surrogate Models](https://mlsanity.com/paper/2311.12796/) (2023)
- [Extracting Deformation-Aware Local Features by Learning to Deform](https://mlsanity.com/paper/2111.10617/) (2021)
- [ROBUSfT: Robust Real-Time Shape-from-Template, a C++ Library](https://mlsanity.com/paper/2301.04037/) (2023)
- [Semantic State Estimation in Cloth Manipulation Tasks](https://mlsanity.com/paper/2203.11647/) (2022)
- [Thin-Shell-SfT: Fine-Grained Monocular Non-rigid 3D Surface Tracking with Neural Deformation Fields](https://mlsanity.com/paper/2503.19976/) (2025)
- [Deformable Surface Reconstruction via Riemannian Metric Preservation](https://mlsanity.com/paper/2212.11596/) (2022)
- [Toward Automatic Interpretation of 3D Plots](https://mlsanity.com/paper/2106.07627/) (2021)
- [Single-view 3D Body and Cloth Reconstruction under Complex Poses](https://mlsanity.com/paper/2205.04087/) (2022)
- [Image-Guided Shape-from-Template Using Mesh Inextensibility Constraints](https://mlsanity.com/paper/2507.22699/) (2025)
- [SAFT: Shape and Appearance of Fabrics from Template via Differentiable Physical Simulations from Monocular Video](https://mlsanity.com/paper/2509.08828/) (2025)
- [Patch-based Representation and Learning for Efficient Deformation Modeling](https://mlsanity.com/paper/2601.05035/) (2026)

## Abstract (arXiv)

We propose a method for predicting the 3D shape of a deformable surface from a single view. By contrast with previous approaches, we do not need a pre-registered template of the surface, and our method is robust to the lack of texture and partial occlusions. At the core of our approach is a it geometry-aware deep architecture that tackles the problem as usually done in analytic solutions: first perform 2D detection of the mesh and then estimate a 3D shape that is geometrically consistent with the image. We train this architecture in an end-to-end manner using a large dataset of synthetic renderings of shapes under different levels of deformation, material properties, textures and lighting conditions. We evaluate our approach on a test split of this dataset and available real benchmarks, consistently improving state-of-the-art solutions with a significantly lower computational time.