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20232026
most citedElasticDiffusion: Training-free Arbitrary Size Image Generation through Global-Local Content Separation

14 citations · 14 across the 9 of their papers we have counts for

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Showing 2025Show all

6 papers · 1 filter

cs.CV2025

EgoEdit: Dataset, Real-Time Streaming Model, and Benchmark for Egocentric Video Editing

Runjia Li, Moayed Haji-Ali, Ashkan Mirzaei +9

We study instruction-guided editing of egocentric videos for interactive AR applications. While recent AI video editors perform well on third-person footage, egocentric views prese…

cs.CV2025

Omni-Attribute: Open-vocabulary Attribute Encoder for Visual Concept Personalization

Tsai-Shien Chen, Aliaksandr Siarohin, Gordon Guocheng Qian +9

Visual concept personalization aims to transfer only specific image attributes, such as identity, expression, lighting, and style, into unseen contexts. However, existing methods r…

cs.CV2025

Sprint: Sparse-Dense Residual Fusion for Efficient Diffusion Transformers

Dogyun Park, Moayed Haji-Ali, Yanyu Li +5

Diffusion Transformers (DiTs) deliver state-of-the-art generative performance but their quadratic training cost with sequence length makes large-scale pretraining prohibitively exp…

cs.CV2025

NoiseShift: Resolution-Aware Noise Recalibration for Better Low-Resolution Image Generation

Ruozhen He, Moayed Haji-Ali, Ziyan Yang +1

Text-to-image diffusion models often degrade when sampled at resolutions outside the final training resolution set. Prior work has largely emphasized higher resolution generation,…

cs.CV2025

Improving Progressive Generation with Decomposable Flow Matching

Moayed Haji-Ali, Willi Menapace, Ivan Skorokhodov +4

Generating high-dimensional visual modalities is a computationally intensive task. A common solution is progressive generation, where the outputs are synthesized in a coarse-to-fin…

cs.CV2025

Evaluating Text-to-Image and Text-to-Video Synthesis with a Conditional Fréchet Distance

Jaywon Koo, Jefferson Hernandez, Moayed Haji-Ali +2

Evaluating text-to-image and text-to-video models is challenging due to a fundamental disconnect: established metrics fail to jointly measure visual quality and semantic alignment…