most citedSAM 2: Segment Anything in Images and Videos

266 citations · 267 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2025

Where Culture Fades: Revealing the Cultural Gap in Text-to-Image Generation

Chuancheng Shi, Shangze Li, Shiming Guo +9

Multilingual text-to-image (T2I) models have advanced rapidly in terms of visual realism and semantic alignment, and are now widely utilized. Yet outputs vary across cultural conte…

cs.CV2025★ 1 cited

DyST-XL: Dynamic Layout Planning and Content Control for Compositional Text-to-Video Generation

Weijie He, Mushui Liu, Yunlong Yu +2

Compositional text-to-video generation, which requires synthesizing dynamic scenes with multiple interacting entities and precise spatial-temporal relationships, remains a critical…

cs.CV2024

RestorerID: Towards Tuning-Free Face Restoration with ID Preservation

Jiacheng Ying, Mushui Liu, Zhe Wu +7

Blind face restoration has made great progress in producing high-quality and lifelike images. Yet it remains challenging to preserve the ID information especially when the degradat…

cs.CV2024★ 266 cited

SAM 2: Segment Anything in Images and Videos

Nikhila Ravi, Valentin Gabeur, Yuan-Ting Hu +15

We present Segment Anything Model 2 (SAM 2), a foundation model towards solving promptable visual segmentation in images and videos. We build a data engine, which improves model an…

cs.CV2024

PointInfinity: Resolution-Invariant Point Diffusion Models

Zixuan Huang, Justin Johnson, Shoubhik Debnath +2

We present PointInfinity, an efficient family of point cloud diffusion models. Our core idea is to use a transformer-based architecture with a fixed-size, resolution-invariant late…