The paper introduces NAIMA, a guided depth super‑resolution framework that leverages global contextual semantic priors from pretrained vision transformer token embeddings. Its Guided Token Attention (GTA) module uses depth encodings as queries to attend over semantic tokens, with a zero‑initialized gate controlling the influence of semantic evidence. NAIMA achieves competitive in‑distribution performance while delivering superior cross‑dataset generalization without relying on decoded priors or auxiliary objectives.
By Tayyab Nasir, Daochang Liu, Ajmal Mian
arXiv:2610.17610v2 Announce Type: new
Abstract: Recently, the integration of generative priors has revolutionized Monocular Depth Estimation (MDE), achieving state-of-the-art (SOTA) performance with...
By Mengke Zhang, Tianyi Yang, Xinzhuo Li, Anlong Ming
WorldGenBench is a benchmark that tests text‑to‑image models on their ability to combine factual grounding with contextual reasoning. It introduces the Knowledge Checklist Score to evaluate whether generated images reflect both explicit and implicit semantic elements. The study finds that while some proprietary models perform well, most open‑source systems struggle, especially with culturally diverse and scientific content, revealing common failure modes such as semantic hallucination and underrepresentation of non‑Western knowledge.
By Ruoshi Xu, Daoan Zhang, Che Jiang, Biaoxiang Chen, Zijian Jin, Yutian Lu, Jianguo Zhang, Liang Yong, Jiebo Luo, Shengda Luo
arXiv:2605. 18714v2 Announce Type: replace-cross Abstract: Unified multimodal models (UMMs) strive to consolidate visual understanding and visual generation within a single architecture.
By Songsong Yu, Yuxin Chen, Ying Shan, Yanwei Li
arXiv:2608.01896v2 Announce Type: replace
Abstract: Existing generative models for earth observation (EO) predominantly rely on fine-tuning natural image priors, which limits their scalability and in...
By Jeonghyeok Do, Munchurl Kim
The paper introduces GeoNeXt, a framework that repurposes pretrained video generative models for geometry estimation by framing it as a next‑frame prediction task. Unlike prior methods that either train separate depth/normal models or fine‑tune image diffusion backbones, GeoNeXt jointly models images and geometric targets, leveraging the structured knowledge of video models for more data‑efficient learning. Experiments show zero‑shot monocular depth and surface normal estimation that outperforms existing generative approaches and rivals discriminative state‑of‑the‑art methods while using far less training data.
By Haosen Yang, Jifei Song, Zhensong Zhang, Xiatian Zhu, Jiankang Deng
The paper introduces OmniVerifier-C1, a graded visual verifier that decomposes complex prompts or environmental rules into fine‑grained components and assigns continuous pointwise scores, moving beyond binary True/False judgments. Experiments show that this structured, interpretable output improves generalization to out‑of‑domain multimodal critic tasks and outperforms binary verifiers in text‑to‑image scenarios. In rule‑governed generative world modeling, the authors demonstrate that a pairwise verification approach yields more accurate critiques than pointwise scoring, with empirical validation on tasks such as Maze and Sudoku.
By Bowei Liu, Xinchen Zhang, Xuhuan Li, Tianlin Zhang, Kaian Jiang, Jingwei Liu, Congyang Zhao, Yifei Qian, Junhong Liu, Zhiheng Li, Yujiu Yang, Yang Cai, Ling Yang
OpenDepth repurposes Image-to-Image (I2I) editing priors to improve monocular depth estimation in open-world conditions. By aligning shallow features in a Diffusion Transformer and adding a joint RGB reconstruction task, the method decouples environmental style from geometry, enabling robust depth inference across varied lighting and weather. The approach achieves new state‑of‑the‑art performance on standard and extreme weather benchmarks, maintaining structural integrity in zero‑shot settings.
arXiv:2602.11499v3 Announce Type: replace
Abstract: Multimodal Large Language Models have shown promising capabilities in bridging visual and textual reasoning, yet their reasoning capabilities in Op...
By Zhenlong Yuan, Yue Wang, Dapeng Zhang, Kejin Cui, Rui Chen, Jing Tang, Lei Sun, Hongwei Yu, Chengxuan Qian, Xiangxiang Chu, Shuo Li, Yuyin Zhou
arXiv:2609.35734v2 Announce Type: replace
Abstract: Novel view synthesis from sparse images must reconcile faithful reconstruction of observed regions with plausible completion of unseen content, whi...
By Kerui Ren, Tao Lu, Linning Xu, Changjian Jiang, Mu Huang, Chunhua Shen, Mulin Yu, Bo Dai
arXiv:2509. 23876v3 Announce Type: replace-cross Abstract: Autoregressive (AR) models based on next-scale prediction have emerged as a powerful tool for image generation, but they face a critical weakness: information inconsistencies between patches across timesteps introduced by progressive resolution scaling.
By Ky Dan Nguyen, Hoang Lam Tran, Anh-Dung Dinh, Daochang Liu, Weidong Cai, Xiuying Wang, Chang Xu
arXiv:2503. 07265v4 Announce Type: replace-cross Abstract: Text-to-Image (T2I) models are capable of generating high-quality artistic creations and visual content.
By Yuwei Niu, Munan Ning, Mengren Zheng, Weiyang Jin, Bin Lin, Peng Jin, Jiaqi Liao, Chaoran Feng, Fanqing Meng, Kunpeng Ning, Bin Zhu, Li Yuan