This page shows the most relevant public items for cs.CV, ranked by trend activity and review signal. Use weekly for fast changes, monthly for more stable patterns, and all-time for evergreen picks.
Paper • Dec 16, 2022 • arXiv • Alex Nichol, Heewoo Jun, Prafulla Dhariwal, Pamela Mishkin, Mark Chen
While text-to-image generation has witnessed rapid progress, text-to-3D synthesis remains challenging due to the lack of massive 3D datasets and the complexity of 3D representations. We introduce P...
Paper • Feb 8, 2026 • arXiv • Zhengqing Gao, Ziwen Li, Xin Wang, Tongliang Liu
To bridge the simulation-to-real gap, we introduce Mirage2Matter, a physically grounded Gaussian world model that generates high-fidelity embodied training data from multi-view videos. We reconstru...
Paper • Feb 12, 2026 • arXiv • Wancai Zheng, Hao Chen, Xinyi Yu
3D Gaussian Splatting (3DGS) is a photorealistic rendering method but lacks semantics and physical executability for Visual-Language Navigation (VLN). We propose SAGE-3D, a paradigm that upgrades 3...
Paper • Feb 12, 2026 • arXiv • Xiang Liu, Guocai Yao, Min Zhang, Changshui Zhang
The goal of this paper is to improve VLA performance through iterative online interaction. Since collecting real-world rollouts is expensive, we investigate whether a learned simulator—an action-co...
Paper • Feb 9, 2026 • arXiv • Minhyuk Lee, Hyekyung Yoon, Myungjoo Kang
Text-to-Image (T2I) diffusion models enable high-quality synthesis, but deployment demands safeguards. We formalize the tension between safety and alignment through a total variation (TV) lens, yie...
Paper • Feb 13, 2026 • arXiv • Ying Yang, Fang Kang, Mengqi Zhang, Zheng Lian
Current unified multimodal models for image generation and editing typically rely on massive parameter scales (>10B), entailing prohibitive costs. We present DeepGen 1.0, a lightweight 5B unified m...
Paper • Feb 13, 2026 • arXiv • Krish Agarwal, Zhuoming Chen, Cheng Luo, Beidi Chen
The quadratic complexity of attention severely limits the context scalability of Video Diffusion Transformers (DiTs). We find that the sparse spatio-temporal attention patterns in Video DiTs can be...
It ranks public content for cs.CV using recent discussion, review, and engagement signals so you can triage faster. This guidance is specific to cs.CV topic page on Attendemia and is written so it still makes sense without reading other sections on the page.
Use weekly for fast-moving updates, monthly for stable trend confirmation, and all-time for evergreen references. This guidance is specific to cs.CV topic page on Attendemia and is written so it still makes sense without reading other sections on the page.
Use the linked entities section to jump to labs, companies, and experts connected to this topic and explore their timelines. This guidance is specific to cs.CV topic page on Attendemia and is written so it still makes sense without reading other sections on the page.
Yes. Use the follow button on this page to subscribe and track new high-signal activity. This guidance is specific to cs.CV topic page on Attendemia and is written so it still makes sense without reading other sections on the page.