{"slug": "generating-artist-like-3d-mesh-topology-via-nearest-vertex-vector-fields", "title": "Generating Artist-Like 3D Mesh Topology via Nearest-Vertex Vector Fields", "summary": "Researchers Haoxuan Li, Ziya Erkoc, Daniele Sirigatti, Vladislav Rosov, Lei Li, Angela Dai and Matthias Niessner introduced TriFlow, a generative method that produces compact 3D meshes with artist-like triangle topology from signed distance field inputs by representing mesh topology as a nearest-vertex vector field (NVF) and training a latent flow-matching model to synthesize it. The team reports TriFlow achieves 90% lower Chamfer Distance and an 8x speedup versus state-of-the-art learning-based approaches, with stronger generalization and improved topology quality. The work is published in the Proceedings of the European Conference on Computer Vision (ECCV) 2026.", "body_md": "Toggle each card to compare the input geometry against the\n                **TriFlow** output (shaded + wireframe). Drag to orbit, scroll to zoom.\n            \n\n                            We present **TriFlow**, a new generative approach for producing compact 3D meshes with\n                            artist-like triangle topology directly from input geometry conditions such as signed\n                            distance fields.\n                        \n\n                            Our key insight is to represent mesh topology as a *nearest-vertex vector field*\n                            (NVF) defined over the surface, where each point encodes its association to the nearest\n                            triangle vertex in the local barycentric frame. We train a latent flow-matching model to\n                            synthesize this field, enabling topology generation conditioned on the input geometry.\n                        \n\nTo extract a coherent mesh, we cluster surface regions using the generated NVF and guide a constrained quadric error metric (QEM) mesh simplification with topology-aware optimization. This yields output meshes that closely match the input geometry while exhibiting structured, artist-like connectivity.\n\n                            Experiments demonstrate that TriFlow achieves stronger generalization and significantly\n                            improved topology quality compared to state-of-the-art learning-based approaches, alongside\n                            **90% lower Chamfer Distance** and an **8× speedup**.\n                        \n\nWe introduce a novel generative approach to create compact, artist-like mesh topologies from signed distance field (SDF) inputs.\n\nOur method consists of three major components:\n\n**TriFlow** can target different topology budgets while keeping the input geometry.\n                Switch between Input and three levels of detail to see how connectivity adapts.\n            \n\nSeveral concurrent efforts also tackle generative mesh topology by modeling a field:\n\n```\n@inproceedings{li2026triflow,\n  title = {TriFlow: Generating Artist-Like 3D Mesh Topology via Nearest-Vertex Vector Fields},\n  author = {Li, Haoxuan and Erko{\\c{c}}, Ziya and Sirigatti, Daniele and Rosov, Vladislav and Li, Lei and Dai, Angela and Nie{\\ss}ner, Matthias},\n  booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},\n  year = {2026},\n}\n```\n\n", "url": "https://wpnews.pro/news/generating-artist-like-3d-mesh-topology-via-nearest-vertex-vector-fields", "canonical_source": "https://derkleineli.github.io/triflow/", "published_at": "2026-09-18 14:13:42+00:00", "updated_at": "2026-09-18 14:26:50.594677+00:00", "lang": "en", "topics": ["artificial-intelligence", "generative-ai", "machine-learning", "computer-vision", "ai-research"], "entities": ["TriFlow", "Haoxuan Li", "Ziya Erkoc", "Daniele Sirigatti", "Vladislav Rosov", "Lei Li", "Angela Dai", "Matthias Niessner"], "alternates": {"html": "https://wpnews.pro/news/generating-artist-like-3d-mesh-topology-via-nearest-vertex-vector-fields", "markdown": "https://wpnews.pro/news/generating-artist-like-3d-mesh-topology-via-nearest-vertex-vector-fields.md", "text": "https://wpnews.pro/news/generating-artist-like-3d-mesh-topology-via-nearest-vertex-vector-fields.txt", "jsonld": "https://wpnews.pro/news/generating-artist-like-3d-mesh-topology-via-nearest-vertex-vector-fields.jsonld"}}