Search arXiv⌕ Search

arXiv · 2610.24569

Energy-minimized interpolatory B-spline lofting for sequences of closed contours

Abstract

B-spline lofting through closed cross-sections is common in reverse engineering, but interpolating each section on its own knot vector can produce an unnecessarily large control net. We construct a common-knot method for closed section curves of odd and even degree by minimizing the strain energy of each section subject to its interpolation constraints. Feasibility for closed section data and uniqueness of the resulting saddle-point solution require full row rank of the periodic collocation matrices, a property that a previously proposed periodic Schoenberg-Whitney window condition does not always guarantee. We establish the exact parity distinction: the condition is sufficient at odd point counts but admits singular configurations at every admissible even point count. For the odd and even degrees used here, the natural and shifting anchor knots are nonsingular; at odd point counts the window condition certifies the selected candidate, whereas at even point counts a rank test with an anchor fallback is applied. The lofting method therefore retains a nonsingular square subsystem for each section and uses nestedness to transfer full row rank to the rectangular common-knot system. Across six closed-contour models the common-knot method uses 83-98% fewer control points than traditional interpolating lofting, with section energies no larger than those of a previous knot-selection method within numerical precision.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Shutao Tang, Zixuan Zhao. 2026-10-17. Energy-minimized interpolatory B-spline lofting for sequences of closed contours. https://arxiv.org/abs/2610.24569

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Quantized High-Performance High-Order Moment-Encoded LBM

Lattice Boltzmann methods (LBM) are widely used for visually rich fluid simulation, but their high memory bandwidth and storage requirements limit GPU performance and scalability, especially when coupled with complex geometry. Recent high-order moment-encoded LBM (HOME-LBM) reduces memory cost by storing compact velocity moments and performing collision directly in moment space, but its tightly coupled boundary treatment introduces divergent control flow. We present a high-performance, quantized HOME-LBM framework for large-scale turbulent fluid-solid simulations on a single GPU. Our geometry-decoupled computation scheme separates the regular fluid update from a sparse solid-correction stage, isolating geometry-dependent computations to improve GPU efficiency. To further reduce storage and memory traffic, we develop a reduced-precision representation for HOME-LBM by combining von Neumann stability analysis, empirical moment-range estimation, and runtime decomposition of second-order moments to control quantization error. We evaluate our approach on real-time demonstrations and large-scale examples using both D3Q19 and D3Q27 lattices, with additional 2D and 3D numerical experiments assessing accuracy and stability. Across the tested benchmarks, our framework substantially accelerates the original FP32 HOME-LBM implementation, with a maximum speedup of 32.5x on the Hilbert-curve geometry. Moment storage is reduced by 50% in fluid-only simulations and 25% in fluid-solid coupling examples, while maintaining numerical stability, fidelity, and high visual quality. In the static delta-wing benchmark, our solver achieves a 5.2x speedup over Leapfrog Flow Maps (LFM) under the tested configuration.

cs.GR↗

StippleDiffusion: Capacity-Constrained Stippling using Controlled Diffusion

Stipple patterns, point sets whose local density tracks a target image, are traditionally produced by per-density iterative optimizers, which are slow, non-differentiable, and must be re-run from scratch for each new target. Learned alternatives have so far addressed only unconditional point generation; capacity-constrained, image-conditioned stippling has remained out of reach. We present the first diffusion-based sampler that simultaneously satisfies a learned local point-distribution prior and a continuous, image-defined capacity constraint at inference. The method is a ControlNet branch built on top of an optimal-transport-grid point-set diffusion baseline, conditioned on the target density map and a high-resolution image. Two design choices make the combination tractable: training and inference are restricted to the late-stage denoising regime, initialized from a density-weighted rejection sample, and the standard zero-convolution injection is replaced with a sigmoid-gated 1x1 projection that preserves the base model's blue-noise structure under hard density signals. A single trained checkpoint accepts arbitrary target densities at inference, generalizes to point budgets that were not seen during training, and produces stipples in time nearly independent of the output point count. On the Icons-50 benchmark, our learned sampler reaches parity with per-density-optimized baselines on every reported metric while remaining differentiable end-to-end.

cs.GR↗

Neural Controlled Directional Frame Generation for Sprite Animation

Sprite-based animation remains prevalent in video game development, but production workflows require capabilities beyond traditional in-betweening. While existing neural interpolation methods can generate intermediate frames, animators also need forward extrapolation to predict motion continuations and backward extrapolation for editing and reverse-engineering existing sprite sheets. Current video generation models either operate on natural video content with stochastic diffusion sampling incompatible with production requirements, or focus exclusively on interpolation without addressing extrapolation tasks. We present a deep learning approach for controlled directional frame generation in sprite animation using three specialized expert models that share identical U-Net encoder-decoder architectures and several domain-specific modifications, including RGBA-aware processing, multi-scale flow refinement, pixel-shuffle upsampling, and alpha-gated blending. Each model is trained on different temporal orderings of the same curated dataset: previous (frames 2,3>1), middle (frames 1,3>2), and next (frames 1,2>3). We demonstrate that only 280 carefully curated sprite triplets enable effective training for all three directional tasks without requiring massive generic datasets. Evaluation on 42 test triplets using the FLIP metric shows that all three models achieve comparable quality--previous (0.26), middle (0.26), and next (0.27)--with no significant degradation for extrapolation versus interpolation, outperforming general-purpose baselines by 17-57%. With deterministic single-pass generation and compact model, our method provides the first practical solution for controllable any-position frame generation specifically tailored for pixel art sprite animation workflows, validating that quality-over-quantity data curation enables specialized deep learning for niche visual domains.

cs.GR↗