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Cosmos 3: Omnimodal World Models for Physical AI
NVIDIA, :, Aditi +293
We introduce Cosmos 3, a family of omnimodal world models designed to jointly process and generate language, image, video, audio, and action sequences within a unified mixture-of-t…
World Simulation with Video Foundation Models for Physical AI
NVIDIA, :, Arslan Ali +87
We introduce [Cosmos-Predict2.5], the latest generation of the Cosmos World Foundation Models for Physical AI. Built on a flow-based architecture, [Cosmos-Predict2.5] unifies Text2…
ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding
Yiyang Zhou, Yangfan He, Yaofeng Su +5
Video understanding is fundamental to tasks such as action recognition, video reasoning, and robotic control. Early video understanding methods based on large vision-language model…
Magma: A Foundation Model for Multimodal AI Agents
Jianwei Yang, Reuben Tan, Qianhui Wu +10
We present Magma, a foundation model that serves multimodal AI agentic tasks in both the digital and physical worlds. Magma is a significant extension of vision-language (VL) model…
BLAPose: Enhancing 3D Human Pose Estimation with Bone Length Adjustment
Chih-Hsiang Hsu, Jyh-Shing Roger Jang
Current approaches in 3D human pose estimation primarily focus on regressing 3D joint locations, often neglecting critical physical constraints such as bone length consistency and…