# Sparse Data Driven Mesh Deformation

arXiv: https://arxiv.org/abs/1709.01250 · HTML: https://mlsanity.com/paper/1709.01250/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Sep 2017.

## Built on by (most cited first, 21 of 21)

- [SC-GS: Sparse-Controlled Gaussian Splatting for Editable Dynamic Scenes](https://mlsanity.com/paper/2312.14937/) (2023)
- [NeRF-Editing: Geometry Editing of Neural Radiance Fields](https://mlsanity.com/paper/2205.04978/) (2022)
- [SceneHGN: Hierarchical Graph Networks for 3D Indoor Scene Generation with Fine-Grained Geometry](https://mlsanity.com/paper/2302.10237/) (2023)
- [Tailor3D: Customized 3D Assets Editing and Generation with Dual-Side Images](https://mlsanity.com/paper/2407.06191/) (2024)
- [Skeleton-free Pose Transfer for Stylized 3D Characters](https://mlsanity.com/paper/2208.00790/) (2022)
- [Mesh-based Gaussian Splatting for Real-time Large-scale Deformation](https://mlsanity.com/paper/2402.04796/) (2024)
- [GLASS: Geometric Latent Augmentation for Shape Spaces](https://mlsanity.com/paper/2108.03225/) (2021)
- [Edit360: 2D Image Edits to 3D Assets from Any Angle](https://mlsanity.com/paper/2506.10507/) (2025)
- [Learnable Infinite Taylor Gaussian for Dynamic View Rendering](https://mlsanity.com/paper/2412.04282/) (2024)
- [A Deep Signed Directional Distance Function for Object Shape Representation](https://mlsanity.com/paper/2107.11024/) (2021)
- [3D-Consistent Human Avatars with Sparse Inputs via Gaussian Splatting and Contrastive Learning](https://mlsanity.com/paper/2408.09663/) (2024)
- [Deep Deformation Detail Synthesis for Thin Shell Models](https://mlsanity.com/paper/2102.11541/) (2021)
- [Learning Mesh Representations via Binary Space Partitioning Tree Networks](https://mlsanity.com/paper/2106.14274/) (2021)
- [Active Learning of Neural Collision Handler for Complex 3D Mesh Deformations](https://mlsanity.com/paper/2110.07727/) (2021)
- [DiffSurf: A Transformer-based Diffusion Model for Generating and Reconstructing 3D Surfaces in Pose](https://mlsanity.com/paper/2408.14860/) (2024)
- [StylePart: Image-based Shape Part Manipulation](https://mlsanity.com/paper/2111.10520/) (2021)
- [Deep Spectral Meshes: Multi-Frequency Facial Mesh Processing with Graph Neural Networks](https://mlsanity.com/paper/2402.10365/) (2024)
- [Refined Geometry-guided Head Avatar Reconstruction from Monocular RGB Video](https://mlsanity.com/paper/2503.21886/) (2025)
- [Disentangled Human Body Representation Based on Unsupervised Semantic-Aware Learning](https://mlsanity.com/paper/2505.19049/) (2025)
- [Handle-based Mesh Deformation Guided By Vision Language Model](https://mlsanity.com/paper/2506.04562/) (2025)
- [P-CORE: Self-Supervised Surface Consistency for Point-Based Neural Editing](https://mlsanity.com/paper/2609.03349/) (2026)

## Abstract (arXiv)

Example-based mesh deformation methods are powerful tools for realistic shape editing. However, existing techniques typically combine all the example deformation modes, which can lead to overfitting, i.e. using a overly complicated model to explain the user-specified deformation. This leads to implausible or unstable deformation results, including unexpected global changes outside the region of interest. To address this fundamental limitation, we propose a sparse blending method that automatically selects a smaller number of deformation modes to compactly describe the desired deformation. This along with a suitably chosen deformation basis including spatially localized deformation modes leads to significant advantages, including more meaningful, reliable, and efficient deformations because fewer and localized deformation modes are applied. To cope with large rotations, we develop a simple but effective representation based on polar decomposition of deformation gradients, which resolves the ambiguity of large global rotations using an as-consistent-as-possible global optimization. This simple representation has a closed form solution for derivatives, making it efficient for sparse localized representation and thus ensuring interactive performance. Experimental results show that our method outperforms state-of-the-art data-driven mesh deformation methods, for both quality of results and efficiency.