# Fuse3D: Generating 3D Assets Controlled by Multi-Image Fusion

arXiv: https://arxiv.org/abs/2602.17040 · HTML: https://mlsanity.com/paper/2602.17040/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Nov 2025.

## Built on by (most cited first, 4 of 4)

- [UMI3D: Robust 3D Generation on Unconstrained Multi-Image Inputs via Simultaneous Focus Cross-Attention Routing](https://mlsanity.com/paper/2607.24298/) (2026)
- [JointEdit3D: Feed-Forward 3D Scene Editing in a Unified Latent Space](https://mlsanity.com/paper/2606.13345/) (2026)
- [HIVE-3D: Hierarchical Voxel Enhancement for High-Quality 3D Scene Generation](https://mlsanity.com/paper/2607.13468/) (2026)
- [ES3D: Embedding Semantics into 3D Space for Component-Aware Editing](https://mlsanity.com/paper/2608.15749/) (2026)

## Abstract (arXiv)

Recently, generating 3D assets with the control of condition images has achieved impressive quality. However, existing 3D generation methods are limited to handling a single control objective and lack the ability to utilize multiple images to independently control different regions of a 3D asset, which hinders their flexibility in applications. We propose Fuse3D, a novel method that enables generating 3D assets under the control of multiple images, allowing for the seamless fusion of multi-level regional controls from global views to intricate local details. First, we introduce a Multi-Condition Fusion Module to integrate the visual features from multiple image regions. Then, we propose a method to automatically align user-selected 2D image regions with their associated 3D regions based on semantic cues. Finally, to resolve control conflicts and enhance local control features from multi-condition images, we introduce a Local Attention Enhancement Strategy that flexibly balances region-specific feature fusion. Overall, we introduce the first method capable of controllable 3D asset generation from multiple condition images. The experimental results indicate that Fuse3D can flexibly fuse multiple 2D image regions into coherent 3D structures, resulting in high-quality 3D assets. Code and data for this paper are at https://jinnmnm.github.io/Fuse3d.github.io/.