uv parameterization implementation

This study addresses the reliance on manual intervention for seam planning in production-level automatic UV unwrapping of quadrilateral meshes. We propose a training-free agent-based method that leverages vision-language models (VLMs) integrated with domain knowledge. By employing query-based mesh representations and a domain-specific language (DSL), our approach decouples high-level intent planning from low-level edge selection, while introducing a feedback loop mechanism to iteratively refine seams. This design ensures compatibility across backend VLMs and enables scalability to extremely large meshes. Experimental results demonstrate that the proposed method reduces the number of charts by 2.9× and shortens seam length by 1.63×, achieving an 80.9% preference rate among professional artists.

This work addresses the problem of local parameterization of implicit surfaces—such as neural implicit fields and point clouds—at arbitrary query points. We propose a geodesic spline-based parameterization method that relies solely on the signed distance function (SDF) and its projection operator, without requiring meshes, surface normals, or other auxiliary geometric data. Our method employs a two-stage radial sampling and B-spline interpolation framework: first, neighborhood points are sampled along geodesic directions; second, a conformal, low-distortion explicit spline surface mapping is constructed. To our knowledge, this is the first unified local parameterization scheme supporting diverse geometric inputs—including neural implicit representations and unstructured point clouds. Experiments demonstrate significant improvements in robustness and generality for local texture mapping and interactive curve drawing on implicit surfaces. The approach establishes a new paradigm for real-time editing and visualization of implicit geometry.

Current 3D texture generation heavily relies on manual UV mapping—time-consuming and lacking semantic awareness and visibility considerations. To address this, we propose the first unsupervised, differentiable UV parameterization framework that jointly incorporates semantic consistency and visibility awareness. Our method (1) achieves semantically coherent UV chart decomposition via mesh semantic segmentation and cross-shape semantic alignment; (2) introduces ambient occlusion (AO)-weighted soft seam optimization to implicitly guide cuts toward low-visibility regions; and (3) designs an end-to-end trainable backbone that jointly optimizes UV parameterization and seam distribution. Quantitative and qualitative evaluations across multiple benchmarks demonstrate that our approach significantly reduces visible seam artifacts and substantially improves downstream texture generation quality and visual naturalness. This work establishes a new paradigm for automated, high-fidelity 3D content generation.

Addressing the challenge of simplifying non-manifold, multiply-connected, and textured triangle meshes, this paper formulates mesh simplification as a 2-dimensional simplicial complex reduction problem—the first such formulation—and introduces a topology-robust edge-collapse framework. Key contributions include: (1) an enhanced quadric error metric adapted to topological changes, ensuring geometric fidelity; (2) a novel texture simplification paradigm that retains only texture colors while decoupling UV layout optimization, thereby eliminating bleeding artifacts entirely; and (3) a color-space-driven texture remapping strategy. The method supports arbitrary topology and level-of-detail (LOD) generation. Comprehensive qualitative and quantitative evaluations, together with user studies, demonstrate consistent superiority over state-of-the-art approaches—significantly improving simplification quality and visual consistency for non-manifold meshes.

This work addresses key limitations in text-to-3D material generation—namely, heavy reliance on large-scale 3D-text paired data, limited editability, and insufficient photorealistic rendering fidelity. We propose an end-to-end framework that operates without 3D-text paired supervision. Our core innovations are threefold: (1) adopting procedural material graphs—not conventional texture maps—as the underlying material representation; (2) designing a segment-wise controlled diffusion model integrated with differentiable rendering to jointly optimize material parameters under text guidance; and (3) enabling fine-grained semantic control via geometric segmentation, text-guided 2D diffusion priors, and material graph parameter initialization. Experiments demonstrate substantial improvements over prior methods in realism, resolution, and interactive editability. The framework supports real-time, high-fidelity material synthesis and flexible, intuitive parameter adjustments—marking a significant step toward controllable, photorealistic text-driven material generation.

Traditional UV unwrapping methods struggle to simultaneously minimize geometric distortion and satisfy artists’ stylistic preferences, such as straight seams and axis-aligned UV islands. This work formulates UV unwrapping for the first time as an end-to-end flow-matching generative problem, learning a mesh-conditioned transport process that maps noise to artist-style UV layouts, thereby producing diverse, production-ready results. To bridge the gap between geometric fidelity and artistic style, the authors introduce a boundary-aware loss and a model-in-the-loop fine-tuning mechanism. Evaluated on a large-scale professional dataset, the proposed method significantly outperforms existing approaches, generating notably straighter seams and more compact, axis-aligned UV islands while maintaining low distortion—results that achieve strong approval from professional artists.

This work addresses the limitations of traditional UV parametrization methods, which are prone to poor initialization, local minima, and topological foldovers that compromise mapping validity under a fixed atlas. The authors reformulate the problem as a continuous neural reparameterization task, leveraging an untrained SIREN network to implicitly map vertex features into UV space, with its weights optimized via geometric energy minimization. Key innovations include using Laplace–Beltrami spectral coordinates as input, Tutte residual warm-starting, a C² determinant expansion, an injectivity barrier, and a validity-check fallback mechanism, collectively forming a verification-first robust solver. Experiments demonstrate that the method achieves 42 and 47 flip-free valid parametrizations on Thingi10K and xatlas-cut benchmarks, respectively—all compact atlases being flip-free—and attains 1,000/1,000 strictly locally valid, flip-free UV atlases on the Amara Spatial dataset.

This work proposes a novel surface representation framework that addresses the lack of effective smooth interpolation methods for non-quadrilateral faces in arbitrarily topologized closed meshes. By integrating local polygonal quadratic interpolation with rational curve parameterization, the method constructs smoothly connected quadrilateral patches and introduces a specialized rational-curve-based parameterization strategy for triangular and general polygonal faces. Through sub-patch blending and surface stitching techniques, the approach achieves globally C¹-continuous, high-quality surface reconstruction. Notably, this is the first unified framework capable of effectively handling smooth interpolation across faces with arbitrary numbers of edges, significantly enhancing both the quality and flexibility of surface generation for complex-topology meshes.

This work addresses the challenge of preserving mapping continuity and bijectivity during complex remeshing processes, where conventional data transfer methods often induce geometric or attribute distortions. The authors propose a composite mapping framework based on local bijective atlases, enhanced by a Shared Scaffold structure that guarantees global bijectivity. The approach is generalized to support a variety of remeshing operations and, for the first time, enables the construction of bijective mappings on 3D tetrahedral remeshings by innovatively integrating Steinitz’s theorem with Maxwell–Cremona lifting theory. This framework facilitates precise tracking of geometric entities—including points, curves, and surfaces—across remeshing sequences, significantly improving fidelity in high-precision applications such as texture transfer and volumetric simulation.

This work proposes a novel and efficient method for evaluating Bézier curves and their higher-order generalizations—including surfaces, volumes, B-splines, and NURBS—on GPUs. By reformulating curve evaluation as texture lookup operations, the approach systematically leverages the GPU’s fixed-function linear texture interpolation hardware for the first time, effectively offloading computational workload from shaders to texture units. The method further enhances performance by integrating Seiler interpolation. Compared to conventional shader-based polynomial evaluation schemes, it achieves substantial speedups while preserving numerical accuracy, making it particularly well-suited for compute-constrained GPU environments.