cs.GRApr 9, 2026
SaveMeshOn: Intersection-Free Mesh-to-Mesh Composition
Organizations: Columbia University · University of Chicago · Cornell University
Abstract
We propose MeshOn, a method that finds physically and semantically realistic compositions of two input meshes. Given an accessory, a base mesh with a user-defined target region, and optional text strings for both meshes, MeshOn uses a multi-step optimization framework to realistically fit the meshes onto each other while preventing intersections. We initialize the shapes' rigid configuration via a structured alignment scheme using Vision-to-Language Models, which we then optimize using a combination of attractive geometric losses, and a physics-inspired barrier loss that prevents surface intersections. We then obtain a final deformation of the object, assisted by a diffusion prior. Our method successfully fits accessories of various materials over a breadth of target regions, and is designed to fit directly into existing digital artist workflows. We demonstrate the robustness and accuracy of our pipeline by comparing it with generative approaches and traditional registration algorithms.
Figures & tables
Figure 2: Digital artists often load shapes from pre-existing libraries of meshes that include additional information like animation rigs, skinning weights, texture maps and more. Generative shape editing methods like Instant3dit [ 4 ] discard this information, performing global changes to the shapes and merging them together. MeshOn is designed to fit exactly within this common artistic pipeline, and perfectly preserves all pre-existing information on the input meshes.
Figure 3: We compose the two meshes through a multi-step optimization pipeline: from an initialization obtained with a Vision-to-Language Model ( subsection 3.5 ), we start by obtaining a tight fit containing intersections ( subsection 3.1 ), which we then resolve ( subsection 3.2 ). After finetuning the fit to obtain the best possible rigid fit ( subsection 3.3 ), we improve it further by allowing small deformations in the accessory object ( subsection 3.4 ).
Figure 4: MeshOn is capable of fitting a wide variety of accessories on a range of different meshes. Our method handles challenging spatial relationships such as sliding glasses along a head to align with the ears, positioning hats and helmets to conform to head curvature, wrapping bands around articulated limbs, and fitting objects along long, curved surfaces. These examples demonstrate the flexibility of our fitting trajectory and its ability to adapt accessories to widely varying shapes and poses.
Figure 5:
Figure 6: MeshOn is capable of fitting the accessory onto the base mesh in a way that considers material properties. Instead of having to specify the numeric material parameters, artists can provide material guidance through text prompts, that get combined with rendered images and interpreted by a VLM to output specific elastic parameters.
Figure 7: MeshOn uses a multi-step process, where each of them is critical for achieving a desirable mesh to mesh composition result. Omitting any of the steps yields a semantically (two left results) or physically (two center results) unplausible solutions. Utilizing Step 4 further improves the result (second from the right), allowing the asset to deform and better fit the base mesh (rightmost).
Figure 8:
Figure 9: Our method is moderately robust to different initialization configurations, although it can fail to converge to the desired output in very adversarial cases (leftmost and rightmost). In subsection 3.5 , we introduce a VLM initialization strategy that avoids these cases.
Figure 10: Region-controlled composition. (a) Given four distinct user-selected target regions, MeshOn places a ring precisely on each selected region, demonstrating explicit user control over the fitting process. (b) Because the method adheres strictly to the selected region, asset placement is sensitive to the region definition; for example, glasses sit naturally on the ears only if a small portion of the ear region is included in the selection.
Figure 11: Unlike ours (left), classical registration algorithms like ICP contain no semantic guidance: therefore, they will produce unrealistic configurations; e.g., glasses that sit on the eyes upside down. These algorithms are also not designed to avoid intersections; therefore, they will produce many of them (see blowups). See supplementary materials for details of these methods.
| Alignment Baselines | ||||||
| Metric | Instant3Dit | Best ICP | Best FGR | RANSAC+ICP | RANSAC+FGR | Ours |
| CLIP ( ) | 0.311 | 0.341 | 0.333 | 0.331 | 0.321 | 0.356 |
| CLIP-IQA ( ) | 0.559 | 0.586 | 0.609 | 0.608 | 0.610 | 0.604 |
| VQA ( ) | 65.62 | 73.71 | 73.97 | 73.72 | 73.68 | 74.49 |
| Ablation Study | ||||||
Table 1: Quantitative evaluation using CLIP, CLIP-IQA, and VQA scores. Top: comparison against alignment baselines. Bottom: ablation study of our multi-step pipeline. Scores are averaged over per-example best renderings (best of 4); higher is better.
| Ablation Study | |||||
| Metric | Best ICP | Best FGR | RANSAC+ICP | RANSAC+FGR | Ours |
| Intersecting faces ( ) | 628 | 660 | 544 | 434 | 0 |
| Max penetration ( ) | 0.0197 | 0.0237 | 0.0243 | 0.0237 | 0 |
Table 2: Penetration statistics averaged over ten experiments. We report the total number of intersecting faces and the maximum penetration depth. Our method produces intersection-free compositions (all zeros).
Appendix figures & tables2 assets
Supplementary material from the paper’s appendix.
Appendix
Figure 12: We show a comparison of runtime and GPU memory consumption for calculating a single distance measure using our GPU-optimized BVH structure. We run the calculation with varying resolutions of the base mesh (visualized on top of each mesh), without using any user defined masks. We use a target mesh with a constant size of 8646 vertices and 17288 faces.
| User Study Results | ||||||
| Statistic | Instant3Dit | Best ICP | Best FGR | RANSAC+ICP | RANSAC+FGR | Ours |
| Average Preference (%) | 2.81 | 3.13 | 0.63 | 0.00 | 0.63 | 92.81 |
| Minimum (%) | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 81.25 |
| Maximum (%) | 15.63 | 12.50 | 3.13 | 0.00 | 3.13 | 100.00 |
Table 3: User study results comparing six methods for the 3D wearable compositing task. Each entry reports the percentage of participant selections received by each method across all evaluation prompts (32 participants per prompt). We report the average preference percentage, as well as the minimum and maximum percentage achieved by each method on any individual example.