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 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 introduces Hunyuan3D-Buffalo 1.0, a unified model for 3D generation, understanding, and editing, and demonstrates its state-of-the-art performance on various benchmarks. Practitioners may care about this work because it provides a scalable framework for 3D modeling and editing tasks.
This paper develops a new AI model that can efficiently search through vast spaces of possibilities, such as designing molecules or optimizing neural networks, by combining a generative model with a model that estimates the potential performance of each candidate. Practitioners might care about this approach because it can significantly speed up the discovery process and improve the quality of the results.