A Dive into Text-to-Video Models

PRX Part 3 — Training a Text-to-Image Model in 24h!

Training Design for Text-to-Image Models: Lessons from Ablations

Video generation models as world simulators

We explore large-scale training of generative models on video data. Specifically, we train text-conditional diffusion models jointly on videos and images of variable durations, resolutions and aspect ratios.

M4V: Multimodal Mamba for Efficient Text-to-Video Generation

arXiv:2506. 10915v2 Announce Type: replace-cross Abstract: Text-to-video generation has significantly enriched content creation and holds the potential to evolve into powerful world simulators.

OSVE: One Step Video Editing with One Step Diffusion Models

arXiv:2607. 19895v1 Announce Type: cross Abstract: Text-guided video editing with diffusion models is impractically slow, hindered by costly multi-step sampling and inversion.

A Systematic Evaluation of Positional Bias in Multi-Video Summarization with MLLMs

Multimodal Large Language Models (MLLMs) are increasingly used for video understanding, yet their reliability under multi-video inputs remains poorly understood. We study positional bias in multi-video summarization, where the quality of a per-video summary can change with the video's input slot even when the underlying content is unchanged.