# SemMorph3D: Unsupervised Semantic-Aware 3D Morphing via Mesh-Guided Gaussians

arXiv: https://arxiv.org/abs/2510.02034 · HTML: https://mlsanity.com/paper/2510.02034/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 45 references that are in the map.
- First posted on arXiv 2 Oct 2025.

## Builds on (most cited first, 20 of 20)

- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [SuGaR: Surface-Aligned Gaussian Splatting for Efficient 3D Mesh Reconstruction and High-Quality Mesh Rendering](https://mlsanity.com/paper/2311.12775/) (2023)
- [Google Scanned Objects: A High-Quality Dataset of 3D Scanned Household Items](https://mlsanity.com/paper/2204.11918/) (2022)
- [Geometric deep learning on graphs and manifolds using mixture model CNNs](https://mlsanity.com/paper/1611.08402/) (2016)
- [Deep Functional Maps: Structured Prediction for Dense Shape Correspondence](https://mlsanity.com/paper/1704.08686/) (2017)
- [Learning shape correspondence with anisotropic convolutional neural networks](https://mlsanity.com/paper/1605.06437/) (2016)
- [NeuroMorph: Unsupervised Shape Interpolation and Correspondence in One Go](https://mlsanity.com/paper/2106.09431/) (2021)
- [DiffMorpher: Unleashing the Capability of Diffusion Models for Image Morphing](https://mlsanity.com/paper/2312.07409/) (2023)
- [Spectral Meets Spatial: Harmonising 3D Shape Matching and Interpolation](https://mlsanity.com/paper/2402.18920/) (2024)
- [AID: Attention Interpolation of Text-to-Image Diffusion](https://mlsanity.com/paper/2403.17924/) (2024)
- [MINA: Convex Mixed-Integer Programming for Non-Rigid Shape Alignment](https://mlsanity.com/paper/2002.12623/) (2020)
- [A Scalable Combinatorial Solver for Elastic Geometrically Consistent 3D Shape Matching](https://mlsanity.com/paper/2204.12805/) (2022)
- [Gaussian Frosting: Editable Complex Radiance Fields with Real-Time Rendering](https://mlsanity.com/paper/2403.14554/) (2024)
- [FreeMorph: Tuning-Free Generalized Image Morphing with Diffusion Model](https://mlsanity.com/paper/2507.01953/) (2025)
- [Denoising Functional Maps: Diffusion Models for Shape Correspondence](https://mlsanity.com/paper/2503.01845/) (2025)
- [Hamiltonian Dynamics for Real-World Shape Interpolation](https://mlsanity.com/paper/2004.05199/) (2020)
- [Shape Non-rigid Kinematics (SNK): A Zero-Shot Method for Non-Rigid Shape Matching via Unsupervised Functional Map Regularized Reconstruction](https://mlsanity.com/paper/2403.06804/) (2024)
- [Stable-SCore: A Stable Registration-based Framework for 3D Shape Correspondence](https://mlsanity.com/paper/2503.21766/) (2025)
- [Textured 3D Regenerative Morphing with 3D Diffusion Prior](https://mlsanity.com/paper/2502.14316/) (2025)
- [Multiview Regenerative Morphing with Dual Flows](https://mlsanity.com/paper/2208.01287/) (2022)

## Built on by (most cited first, 1 of 1)

- [SMAL-pets: SMAL Based Avatars of Pets from Single Image](https://mlsanity.com/paper/2603.17131/) (2026)

## Abstract (arXiv)

We introduce METHODNAME, a novel framework for semantic-aware 3D shape and texture morphing directly from multi-view images. While 3D Gaussian Splatting (3DGS) enables photorealistic rendering, its unstructured nature often leads to catastrophic geometric fragmentation during morphing. Conversely, traditional mesh-based morphing enforces structural integrity but mandates pristine input topology and struggles with complex appearances. Our method resolves this dichotomy by employing a mesh-guided strategy where a coarse, extracted base mesh acts as a flexible geometric anchor. This anchor provides the necessary topological scaffolding to guide unstructured Gaussians, successfully compensating for mesh extraction artifacts and topological limitations. Furthermore, we propose a novel dual-domain optimization strategy that leverages this hybrid representation to establish unsupervised semantic correspondence, synergizing geodesic regularizations for shape preservation with texture-aware constraints for coherent color evolution. This integrated approach ensures stable, physically plausible transformations without requiring labeled data, specialized 3D assets, or category-specific templates. On the proposed TexMorph benchmark, METHODNAME substantially outperforms prior 2D and 3D methods, yielding fully textured, topologically robust 3D morphing while reducing color consistency error (Delta E) by 22.2% and EI by 26.2%. Project page: https://baiyunshu.github.io/GAUSSIANMORPHING.github.io/