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Geração de Imagem

Papers, modelos e datasets em alta no Hugging Face, além do blog oficial — com leitura editorial em português.

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MemoBench: Benchmarking World Modeling in Dynamically Changing Environments

arXiv:2606.27537v1 Announce Type: new Abstract: Video generation models aspire to simulate dynamic environments, and several benchmarks now evaluate memory consistency across frames. However, most assess consistency only while the target remains in view, and the few that force objects out of view evaluate static scenes where nothing changes during occlusion. To bridge this gap, we introduce MemoBench, a diagnostic benchmark built around the disappear-and-reappear paradigm in dynamically changing...

29.06.2026
Blog Geração de Imagem

GeoFace: Consistent Multi-View Face Generation with Geometry-Constrained Diffusion

arXiv:2606.27659v1 Announce Type: new Abstract: We present GeoFace, a geometry-constrained multi-view diffusion framework for consistent face generation from a single input. % While recent multi-view diffusion models achieve photorealistic synthesis at the per-view level, they lack an explicit mechanism to enforce a shared 3D structure across views, often leading to inconsistent geometry across viewpoints. To address this, GeoFace proposes a unified dual-stream framework for joint generation of ...

29.06.2026
Blog Geração de Imagem

CoIn: Comprehensive 2D-3D Inpainting with Gaussian Splatting Guidance

arXiv:2606.27584v1 Announce Type: new Abstract: 3D scene inpainting is essential for reconstructing areas corrupted by occlusions or limited viewpoints. While recent methods leverage Gaussian Splatting (GS) for efficient 3D editing, they often depend on precise multi-view segmentation masks and are inherently constrained to object removal tasks. We propose CoIn, a novel framework that bridges 2D inpainting models and 3DGS through a multi-stage consistency pipeline. Our approach first generates i...

29.06.2026
Blog Geração de Imagem

Boundary condition fidelity for bottom-hole pressure and CO2 plume prediction in geological carbon storage

arXiv:2606.27515v1 Announce Type: new Abstract: Accurate prediction of bottom-hole pressure (BHP) and CO2 plume migration is essential for safe geological carbon storage, yet practical simulations often rely on truncated domains where artificial boundaries distort pressure diffusion and CO2 saturation footprints. In this study, we evaluate how boundary-condition fidelity affects BHP and CO2 plume prediction by comparing ten reduced-domain boundary treatments against full-domain reference simulat...

29.06.2026
Blog LLMs & Texto

DysLexLens: A Low-Resource LLM Framework for Analysing Dyslexic Learners Insights from Online Forums

arXiv:2606.27619v1 Announce Type: new Abstract: Dyslexic learners increasingly use artificial intelligence (AI) tools to support reading, writing, organisation, and study-related tasks. However, their lived experiences with these tools remain largely underexamined. This paper proposes DysLexLens, a low-resource LLM framework, designed to analyse dyslexic learners experience with AI through online forum discussions. DysLexLens is designed as an end-to-end, evidence-traceable architecture which tr...

29.06.2026
Blog Geração de Imagem

Quantum Generative Diffusion Model for Real-World Time Series

arXiv:2606.27561v1 Announce Type: new Abstract: Generative models have achieved remarkable success in data synthesis, though recent advances driven by increasing model scale have introduced challenges in computational cost and efficiency. Quantum machine learning offers a promising alternative, representing complex data distributions using compact, highly expressive models. Here, we propose QDiffusion-TS, the first quantum generative diffusion model for time series synthesis, and validate it on ...

29.06.2026
Blog LLMs & Texto

Masked Language Flow Models

arXiv:2606.27617v1 Announce Type: new Abstract: Masked Diffusion Models (MDMs) promise fast, parallel language generation, but their reverse transition factorises across token positions -- an approximation that breaks down in the few-step sampling regime where parallel generation ought to provide the greatest efficiency gains. Flow Language Models (FLMs) sidestep this limitation by learning a continuous flow that transports noise toward clean sequences represented in Euclidean space, inducing a ...

29.06.2026
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