Filtered to video generation · clear filter
This paper investigates whether video generation models, which are a type of world model, have a basic understanding of object permanence and can be improved through training. Practitioners interested in building human-like physical intelligence may care about this research as it could lead to more realistic and interactive AI systems.
This paper proposes a method to use multimodal large language models (MLLMs) to improve cinematic video generation by providing both temporal inference and factor-aware control. A practitioner might care about this paper if they want to generate more realistic and controllable videos.
This paper introduces a new benchmark for video generation tasks that require both achieving a desired outcome and maintaining semantic consistency with a reference image. Practitioners might care about this research because it can help evaluate and improve the performance of video generation models in real-world applications.
This paper evaluates how well current video generation models can reproduce the distribution of possible behaviors under the same initial observation and action, and whether they can be improved to better align with real-world possibilities. Practitioners caring about realistic and diverse video content might be interested in the findings.
This paper evaluates whether a multimodal generative model can reason about the physical world by testing its ability to integrate information from different modalities, such as text, images, video, and audio. Practitioners might care about this research because it can help develop more advanced models that can better understand and generate complex scenarios.
This paper creates a system that can generate both audio and video simultaneously at high resolution, allowing for more realistic and synchronized content. Practitioners might care about this technology for applications like music videos, live performances, or interactive storytelling.