Filtered to generative models · clear filter
This paper introduces SenseNova-U1.5, a powerful AI model that can understand, reason about, and generate visual content without needing a separate text-to-image model. Practitioners might care because this model can be used to create complex visual content, such as images and videos, with high fidelity and accuracy.
This paper introduces UniEvo-VL, a self-improvement framework for multimodal models that uses their own feedback to learn and improve, without relying on an external teacher. Practitioners might care because it can lead to better image generation capabilities and more efficient models.
This paper develops a model that can automatically generate and adapt agent harnesses, which are crucial for the performance of AI models, to improve their ability to perform tasks. Practitioners might care about this because it could lead to more efficient and effective AI systems.
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.
This paper proposes a method to selectively choose relevant frames from a long generation history to improve video generation, allowing models to efficiently leverage their past content. Practitioners may care about this approach because it can lead to better video quality and consistency.