arXiv subjects

Mihai-Sorin Stupariu

Publications and source records attributed to Mihai-Sorin Stupariu.

4 recordsLinked to original sources

arXiv2026

Stratified Multi-View Aggregation for Score Distillation

Score distillation turns a pretrained 2D diffusion model into a 3D generator, but the per-step gradient is estimated from a single random view: this one-sample estimate has high variance (different views of the same partial scene disagree) and is blind to global shape consistency. Existing multi-view approaches address this by retraining the diffusion prior on multi-view data; this improves consistency but conflates the sampling contribution with the quality of the retrained prior. We instead isolate the sampling axis, leaving the prior frozen. We introduce Multi-View Aggregated Score Distillation (MV-SDI), a training-free sampler that replaces the single-view per-step gradient with an average over K views at a fixed UNet-call budget. Averaging K views lowers the per-step gradient variance toward 1/K of its single-view value. Drawing the K views as antithetic antipodal pairs adds no further variance reduction (measured antipodal correlation rho approximately 0) but stratifies angular coverage (every step covers both hemispheres) removing the same-hemisphere clustering of independent sampling. At a fixed 10,000-UNet-call budget on the 43-prompt SDI benchmark, K=2 halves the optimization steps and raises CLIP R-Precision from 74.8% to 83.8% and CLIP score from 0.297 to 0.312 over the single-view SDI baseline, with consistent gains on HPSv2 and ImageReward and a 0.0% divergence rate. K=4 gives a fourfold step reduction at R-Precision 86.9% and CLIP 0.307. The gains concentrate on hard prompts where single-view distillation collapses, at a measured cost in CLIP-IQA. MV-SDI is drop-in for gradient-based score-distillation pipelines, including Score Distillation via Inversion and plain SDS, and requires no retraining and no multi-view data. Code is available at: https://github.com/marianlupascu/MV-SDI

cs.CV↗

arXiv2025

Optimal Transport for Rectified Flow Image Editing: Unifying Inversion-Based and Direct Methods

Image editing in rectified flow models remains challenging due to the fundamental trade-off between reconstruction fidelity and editing flexibility. While inversion-based methods suffer from trajectory deviation, recent inversion-free approaches like FlowEdit offer direct editing pathways but can benefit from additional guidance to improve structure preservation. In this work, we demonstrate that optimal transport theory provides a unified framework for improving both paradigms in rectified flow editing. We introduce a zero-shot transport-guided inversion framework that leverages optimal transport during the reverse diffusion process, and extend optimal transport principles to enhance inversion-free methods through transport-optimized velocity field corrections. Incorporating transport-based guidance can effectively balance reconstruction accuracy and editing controllability across different rectified flow editing approaches. For inversion-based editing, our method achieves high-fidelity reconstruction with LPIPS scores of 0.001 and SSIM of 0.992 on face editing benchmarks, observing 7.8% to 12.9% improvements over RF-Inversion on LSUN datasets. For inversion-free editing with FlowEdit on FLUX and Stable Diffusion 3, we demonstrate consistent improvements in semantic consistency and structure preservation across diverse editing scenarios. Our semantic face editing experiments show an 11.2% improvement in identity preservation and enhanced perceptual quality. The unified optimal transport framework produces visually compelling edits with superior detail preservation across both inversion-based and direct editing paradigms. Code is available for RF-Inversion and FlowEdit at: https://github.com/marianlupascu/OT-RF

cs.CV↗

arXiv2024

Neural Contrast: Leveraging Generative Editing for Graphic Design Recommendations

Creating visually appealing composites requires optimizing both text and background for compatibility. Previous methods have focused on simple design strategies, such as changing text color or adding background shapes for contrast. These approaches are often destructive, altering text color or partially obstructing the background image. Another method involves placing design elements in non-salient and contrasting regions, but this isn't always effective, especially with patterned backgrounds. To address these challenges, we propose a generative approach using a diffusion model. This method ensures the altered regions beneath design assets exhibit low saliency while enhancing contrast, thereby improving the visibility of the design asset.

cs.CV↗

arXiv2021

ICLR 2021 Challenge for Computational Geometry & Topology: Design and Results

This paper presents the computational challenge on differential geometry and topology that happened within the ICLR 2021 workshop "Geometric and Topological Representation Learning". The competition asked participants to provide creative contributions to the fields of computational geometry and topology through the open-source repositories Geomstats and Giotto-TDA. The challenge attracted 16 teams in its two month duration. This paper describes the design of the challenge and summarizes its main findings.

cs.CG↗