# UniMesh: Unifying 3D Mesh Understanding and Generation

Source: [UniMesh: Unifying 3D Mesh Understanding and Generation](<https://www.sota2.com/research/paper/69e704a57d0172140ec7edf4>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-04-25T06:00:01.522Z

Authors: Peng Huang, Yifeng Chen, Zeyu Zhang, Hao Tang

Published: 2026-04-19T14:53:38.000Z

## Abstract

Recent advances in 3D vision have led to specialized models for either 3D understanding (e.g., shape classification, segmentation, reconstruction) or 3D generation (e.g., synthesis, completion, and editing). However, these tasks are often tackled in isolation, resulting in fragmented architectures and representations that hinder knowledge transfer and holistic scene modeling. To address these challenges, we propose UniMesh, a unified framework that jointly learns 3D generation and understanding within a single architecture. First, we introduce a novel Mesh Head that acts as a cross model interface, bridging diffusion based image generation with implicit shape decoders. Second, we develop Chain of Mesh (CoM), a geometric instantiation of iterative reasoning that enables user driven semantic mesh editing through a closed loop latent, prompting, and re generation cycle. Third, we incorporate a self reflection mechanism based on an Actor Evaluator Self reflection triad to diagnose and correct failures in high level tasks like 3D captioning. Experimental results demonstrate that UniMesh not only achieves competitive performance on standard benchmarks but also unlocks novel capabilities in iterative editing and mutual enhancement between generation and understanding. Code: https://github.com/AIGeeksGroup/UniMesh. Website: https://aigeeksgroup.github.io/UniMesh.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Text-to-Object Generation on DreamFusion prompts](<https://www.sota2.com/research/sota/text-to-object-generation-on-dreamfusion-prompts.md>): Text-to-Object Generation; DreamFusion prompts; CLIP Image-Text Similarity: 0.296.

- [3D Object Captioning on Cap3D subset of 3186 3D objects (test)](<https://www.sota2.com/research/sota/3d-object-captioning-on-cap3d-subset-of-3186-3d-objects-test.md>): 3D Object Captioning; Cap3D subset of 3186 3D objects (test); CLIP Image-Text Score: 0.297.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2604.17472>)

[PDF](<https://arxiv.org/pdf/2604.17472.pdf>)

[Implementation](<https://github.com/AIGeeksGroup/UniMesh>)