# Structure-Aware Shape Synthesis

arXiv: https://arxiv.org/abs/1808.01427 · HTML: https://mlsanity.com/paper/1808.01427/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Aug 2018.

## Built on by (most cited first, 5 of 5)

- [Learning Generative Models of Textured 3D Meshes from Real-World Images](https://mlsanity.com/paper/2103.15627/) (2021)
- [ARAPReg: An As-Rigid-As Possible Regularization Loss for Learning Deformable Shape Generators](https://mlsanity.com/paper/2108.09432/) (2021)
- [MVControl: Adding Conditional Control to Multi-view Diffusion for Controllable Text-to-3D Generation](https://mlsanity.com/paper/2311.14494/) (2023)
- [ET3D: Efficient Text-to-3D Generation via Multi-View Distillation](https://mlsanity.com/paper/2311.15561/) (2023)
- [Attention-based Part Assembly for 3D Volumetric Shape Modeling](https://mlsanity.com/paper/2304.10986/) (2023)

## Abstract (arXiv)

We propose a new procedure to guide training of a data-driven shape generative model using a structure-aware loss function. Complex 3D shapes often can be summarized using a coarsely defined structure which is consistent and robust across variety of observations. However, existing synthesis techniques do not account for structure during training, and thus often generate implausible and structurally unrealistic shapes. During training, we enforce structural constraints in order to enforce consistency and structure across the entire manifold. We propose a novel methodology for training 3D generative models that incorporates structural information into an end-to-end training pipeline.