# MS2Mesh-XR: Multi-modal Sketch-to-Mesh Generation in XR Environments

arXiv: https://arxiv.org/abs/2412.09008 · HTML: https://mlsanity.com/paper/2412.09008/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 39 references that are in the map.
- First posted on arXiv 12 Dec 2024.

## Builds on (most cited first, 25 of 25)

- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [DreamFusion: Text-to-3D using 2D Diffusion](https://mlsanity.com/paper/2209.14988/) (2022)
- [Magic3D: High-Resolution Text-to-3D Content Creation](https://mlsanity.com/paper/2211.10440/) (2022)
- [LRM: Large Reconstruction Model for Single Image to 3D](https://mlsanity.com/paper/2311.04400/) (2023)
- [Fantasia3D: Disentangling Geometry and Appearance for High-quality Text-to-3D Content Creation](https://mlsanity.com/paper/2303.13873/) (2023)
- [Latent-NeRF for Shape-Guided Generation of 3D Shapes and Textures](https://mlsanity.com/paper/2211.07600/) (2022)
- [Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priors](https://mlsanity.com/paper/2306.17843/) (2023)
- [Text2Mesh: Text-Driven Neural Stylization for Meshes](https://mlsanity.com/paper/2112.03221/) (2021)
- [LucidDreamer: Towards High-Fidelity Text-to-3D Generation via Interval Score Matching](https://mlsanity.com/paper/2311.11284/) (2023)
- [CRM: Single Image to 3D Textured Mesh with Convolutional Reconstruction Model](https://mlsanity.com/paper/2403.05034/) (2024)
- [MeshLRM: Large Reconstruction Model for High-Quality Meshes](https://mlsanity.com/paper/2404.12385/) (2024)
- [DreamWaltz: Make a Scene with Complex 3D Animatable Avatars](https://mlsanity.com/paper/2305.12529/) (2023)
- [DreamScene360: Unconstrained Text-to-3D Scene Generation with Panoramic Gaussian Splatting](https://mlsanity.com/paper/2404.06903/) (2024)
- [DreamScene: 3D Gaussian-based Text-to-3D Scene Generation via Formation Pattern Sampling](https://mlsanity.com/paper/2404.03575/) (2024)
- [AvatarVerse: High-quality & Stable 3D Avatar Creation from Text and Pose](https://mlsanity.com/paper/2308.03610/) (2023)
- [Sketch2Scene: Automatic Generation of Interactive 3D Game Scenes from User's Casual Sketches](https://mlsanity.com/paper/2408.04567/) (2024)
- [Advancing Extended Reality with 3D Gaussian Splatting: Innovations and Prospects](https://mlsanity.com/paper/2412.06257/) (2024)
- [PCF-Lift: Panoptic Lifting by Probabilistic Contrastive Fusion](https://mlsanity.com/paper/2410.10659/) (2024)
- [WorldSmith: Iterative and Expressive Prompting for World Building with a Generative AI](https://mlsanity.com/paper/2308.13355/) (2023)
- [VRCopilot: Authoring 3D Layouts with Generative AI Models in VR](https://mlsanity.com/paper/2408.09382/) (2024)
- [Energy-Based Residual Latent Transport for Unsupervised Point Cloud Completion](https://mlsanity.com/paper/2211.06820/) (2022)
- [Semi-signed prioritized neural fitting for surface reconstruction from unoriented point clouds](https://mlsanity.com/paper/2206.06715/) (2022)
- [Structure-Aware 3D VR Sketch to 3D Shape Retrieval](https://mlsanity.com/paper/2209.09043/) (2022)
- [Magic3DSketch: Create Colorful 3D Models From Sketch-Based 3D Modeling Guided by Text and Language-Image Pre-Training](https://mlsanity.com/paper/2407.19225/) (2024)
- [StripBrush: A Constraint-Relaxed 3D Brush Reduces Physical Effort and Enhances the Quality of Spatial Drawing](https://mlsanity.com/paper/2109.03845/) (2021)

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

- [When Generative Artificial Intelligence meets Extended Reality: A Systematic Review](https://mlsanity.com/paper/2511.03282/) (2025)

## Abstract (arXiv)

We present MS2Mesh-XR, a novel multi-modal sketch-to-mesh generation pipeline that enables users to create realistic 3D objects in extended reality (XR) environments using hand-drawn sketches assisted by voice inputs. In specific, users can intuitively sketch objects using natural hand movements in mid-air within a virtual environment. By integrating voice inputs, we devise ControlNet to infer realistic images based on the drawn sketches and interpreted text prompts. Users can then review and select their preferred image, which is subsequently reconstructed into a detailed 3D mesh using the Convolutional Reconstruction Model. In particular, our proposed pipeline can generate a high-quality 3D mesh in less than 20 seconds, allowing for immersive visualization and manipulation in run-time XR scenes. We demonstrate the practicability of our pipeline through two use cases in XR settings. By leveraging natural user inputs and cutting-edge generative AI capabilities, our approach can significantly facilitate XR-based creative production and enhance user experiences. Our code and demo will be available at: https://yueqiu0911.github.io/MS2Mesh-XR/