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20232025
most citedMovie Gen: A Cast of Media Foundation Models

8 citations · 10 across the 8 of their papers we have counts for

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cs.CV2024★ 8 cited

Movie Gen: A Cast of Media Foundation Models

Adam Polyak, Amit Zohar, Andrew Brown +85

We present Movie Gen, a cast of foundation models that generates high-quality, 1080p HD videos with different aspect ratios and synchronized audio. We also show additional capabili…

cs.CV2024

Layout Agnostic Scene Text Image Synthesis with Diffusion Models

Qilong Zhangli, Jindong Jiang, Di Liu +6

While diffusion models have significantly advanced the quality of image generation their capability to accurately and coherently render text within these images remains a substanti…

cs.CV2024

Towards Artwork Explanation in Large-scale Vision Language Models

Kazuki Hayashi, Yusuke Sakai, Hidetaka Kamigaito +2

Large-scale Vision-Language Models (LVLMs) output text from images and instructions, demonstrating capabilities in text generation and comprehension. However, it has not been clari…

cs.CV2024★ 1 cited

Animated Stickers: Bringing Stickers to Life with Video Diffusion

David Yan, Winnie Zhang, Luxin Zhang +15

We introduce animated stickers, a video diffusion model which generates an animation conditioned on a text prompt and static sticker image. Our model is built on top of the state-o…

cs.CV2023

Text-to-Sticker: Style Tailoring Latent Diffusion Models for Human Expression

Animesh Sinha, Bo Sun, Anmol Kalia +14

We introduce Style Tailoring, a recipe to finetune Latent Diffusion Models (LDMs) in a distinct domain with high visual quality, prompt alignment and scene diversity. We choose sti…

cs.CV2023

CiT: Curation in Training for Effective Vision-Language Data

Hu Xu, Saining Xie, Po-Yao Huang +5

Large vision-language models are generally applicable to many downstream tasks, but come at an exorbitant training cost that only large institutions can afford. This paper trades g…