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cs.CV2026

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…

cs.CV2026

Deformba: Vision State Space Model with Adaptive State Fusion

Hongyu Ke, Jack Morris, Yongkang Liu +4

State Space Models (SSMs) have emerged as a powerful and efficient alternative to Transformers, demonstrating linear-time complexity and exceptional sequence modeling capabilities.…

cs.CV2026

InEdit-Bench: Benchmarking Intermediate Logical Pathways for Intelligent Image Editing Models

Zhiqiang Sheng, Xumeng Han, Zhiwei Zhang +6

Multimodal generative models have made significant strides in image editing, demonstrating impressive performance on a variety of static tasks. However, their proficiency typically…

cs.CV2026

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…

cs.CV2025

ArtiScene: Language-Driven Artistic 3D Scene Generation Through Image Intermediary

Zeqi Gu, Yin Cui, Zhaoshuo Li +6

Designing 3D scenes is traditionally a challenging task that demands both artistic expertise and proficiency with complex software. Recent advances in text-to-3D generation have gr…

cs.CV2025

Scenethesis: A Language and Vision Agentic Framework for 3D Scene Generation

Lu Ling, Chen-Hsuan Lin, Tsung-Yi Lin +7

Synthesizing interactive 3D scenes from text is essential for gaming, virtual reality, and embodied AI. However, existing methods face several challenges. Learning-based approaches…