Feb 2025· SIAM Journal on Scientific Computing· Vol abs/2502.16445, pp. C814-C831· 3 citations· 50 references
Computer ScienceMathematics
TL;DR
This work explores image generation using flow matching using flow matching and proposes an iterative process that can be integrated into virtually any generative modeling technique, thereby enhancing the performance and robustness of image synthesis systems.
Abstract
Abstract.
Generative models for image generation are now commonly used for a wide variety of applications, ranging from guided image generation for entertainment to solving inverse problems. Nonetheless, training a generator is a nontrivial feat that requires fine-tuning and can lead to so-called hallucinations, that is, the generation of images that are unrealistic. In this work, we explore image generation using flow matching. We explain and demonstrate why flow matching can generate hallucinations, and we propose an iterative process to improve the generation process. Our iterative process can be integrated into virtually any generative modeling technique, thereby enhancing the performance and robustness of image synthesis systems.
This paper presents an innovative method that leverages user-specified action paths to guide the 4D scene generation that dynamically synchronizes motions in the action path domain with their corresponding contents in the time domain.
Guo-Wei Yang, Qun-Ce Xu, Zhao Wei et al.· Science China Information Sc...· 0 citations
SpatialCrafter is presented, a novel two-stage framework that addresses explorable image-to-scene generation issues by introducing a global 3D proxy for high-fidelity image-to-scene generation and appearance refinement and introduces Parallel Geometry Injection and Proxy-Aware Corruption training strategies.
Chuan Fang, Lingteng Qiu, Yixun Liang et al.· 1 citation
Image synthesis has become a central problem in generative AI, with applications spanning virtual reality, medical imaging, autonomous systems, and creative content generation. Diffusion-based generative models have substantially advanced the field by producing high-fidelity, visually consistent outputs, yet a fundamen...
Kanika Verma, Khushi Aggarwal, Pradipti Singh et al.· International Conference Com...· 0 citations
The results show that careful tokenizer--backbone--system co-design can deliver strong high-resolution generation and editing within an efficient 4B model family.
RefineSVG, a single-step closed-loop visual feedback framework that enables multimodal large language models (MLLMs) to perform high-fidelity image-to-SVG generation through self-correction, consistently outperforms existing baselines in reconstruction fidelity, structural accuracy, and code efficiency.
GenRec is introduced, a multi-view flow matching model that builds the reconstruction--generation split directly into its architecture, supervision, and gradient flow, and attains the best reconstruction fidelity in observed regions while also surpassing purely generative baselines on perceptual quality in unobserved o...
Ata Çelen, Jaewoo Jung, Federico Tombari et al.· 0 citations
Machine-learning networks can learn without detailed instructions from a designer. Those that do may offer insight into how the brain reorganizes itself during learning.
Exploring how generative AI could make machine vision more accessible to businesses. The post GenEye in a Box: Making Machine Vision Something You Can Just Ask For appeared first on GPT-Lab.
MIT News · Artificial Intelligence· news.mit.eduSep 29, 2026
Professor Sherry Turkle’s new book, “Artificial Intimacy,” offers a withering critique of chatbots and the antisocial dynamics she believes they encourage.
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
