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cs.CV2025

Captain Cinema: Towards Short Movie Generation

Junfei Xiao, Ceyuan Yang, Lvmin Zhang +7

We present Captain Cinema, a generation framework for short movie generation. Given a detailed textual description of a movie storyline, our approach firstly generates a sequence o…

cs.CV2025

VINCIE: Unlocking In-context Image Editing from Video

Leigang Qu, Feng Cheng, Ziyan Yang +7

In-context image editing aims to modify images based on a contextual sequence comprising text and previously generated images. Existing methods typically depend on task-specific pi…

cs.CV2025

Seaweed-7B: Cost-Effective Training of Video Generation Foundation Model

Team Seawead, Ceyuan Yang, Zhijie Lin +52

This technical report presents a cost-efficient strategy for training a video generation foundation model. We present a mid-sized research model with approximately 7 billion parame…

cs.CV2025

VoQA: Visual-only Question Answering

Jianing An, Luyang Jiang, Jie Luo +2

Visual understanding requires interpreting both natural scenes and the textual information that appears within them, motivating tasks such as Visual Question Answering (VQA). Howev…

cs.CV2025

Long Context Tuning for Video Generation

Yuwei Guo, Ceyuan Yang, Ziyan Yang +5

Recent advances in video generation can produce realistic, minute-long single-shot videos with scalable diffusion transformers. However, real-world narrative videos require multi-s…

cs.CV2025

UVE: Are MLLMs Unified Evaluators for AI-Generated Videos?

Yuanxin Liu, Rui Zhu, Shuhuai Ren +4

With the rapid growth of video generative models (VGMs), it is essential to develop reliable and comprehensive automatic metrics for AI-generated videos (AIGVs). Existing methods e…