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首次观测 · 记录 #1CVF · CVPR 2026:cvf:0730ba8690d26de9da6f3fff20d9eeebCVF · CVPR 2026

Yume1.5: A Text-Controlled Interactive World Generation Model

Mao, Xiaofeng, Li, Zhen, Li, Chuanhao, Xu, Xiaojie, Ying, Kaining, Zhang, Kaipeng

原文摘要

Recent approaches have demonstrated the promise of using diffusion models to generate interactive and explorable worlds. However, most of these methods face critical challenges such as excessively large parameter sizes, reliance on lengthy inference steps, and rapidly growing historical context, which severely limit real-time performance and lack text-controlled generation capabilities.To address these challenges, we propose Yume1.5, a novel framework designed to generate realistic, interactive, and continuous worlds from a single image or text prompt. Yume1.5 achieves this through a carefully designed framework that supports keyboard-based exploration of the generated worlds. The framework comprises three core components: (1) a long-video generation method combining unified context compression and linear attention; (2) a context compression-based bidirectional attention distillation approach with an enhanced text embedding scheme for real-time streaming video generation. Yume1.5 achieves an average generation speed of 12 fps at 540p resolution using only a single A100 GPU; (3) a text-controlled method for generating world events. Github: https://github.com/stdstu12/YUME.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:5c618f1e96cff3b225d24f59c95ee7aeCVF · CVPR 2026

SVBench: Evaluation of Video Generation Models on Social Reasoning

Peng, Wenshuo, Wang, Gongxuan, Yang, Tianmeng, Li, Chuanhao, Xu, Xiaojie, He, Hui, Zhang, Kaipeng

原文摘要

Recent text-to-video generation models have made remarkable progress in visual realism, motion fidelity, and text-video alignment, yet they still struggle to produce socially coherent behavior. Unlike humans, who readily infer intentions, beliefs, emotions, and social norms from brief visual cues, current models often generate literal scenes without capturing the underlying causal and psychological dynamics. To systematically assess this limitation, we introduce the first benchmark for social reasoning in video generation. Grounded in developmental and social psychology, the benchmark covers thirty classic social cognition paradigms spanning seven core dimensions: mental-state inference, goal-directed action, joint attention, social coordination, prosocial behavior, social norms, and multi-agent strategy. To operationalize these paradigms, we build a fully training-free agent-based pipeline that distills the reasoning structure of each paradigm, synthesizes diverse video-ready scenarios, enforces conceptual neutrality and difficulty control through cue-based critique, and evaluates generated videos with a high-capacity VLM judge along five interpretable dimensions of social reasoning. Using this framework, we conduct the first large-scale evaluation of seven state-of-the-art video generation systems. Results show a clear gap between surface-level plausibility and deeper social reasoning, suggesting that current models remain limited in their ability to generate socially grounded behavior. https://github.com/Gloria2tt/SVBench-Evaluation

cs.CV · cs.MA

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:8fbf5a01e076977b3f0eac0db62339a2CVF · CVPR 2026

MatchMask: Mask-Centric Generative Data Augmentation for Label-Scarce Semantic Segmentation

Lin, Yuqi, Zhang, Hao, Shao, Wenqi, Liu, Shiqu, Gu, Zhihong, Wang, Wenxiao, He, Xiaofei, Zhang, Kaipeng

原文摘要

Current semantic segmentation models are very data-hungry and require massive costly pixel-wise human annotations. Generative data augmentation, which scales the train set using generative models, provides a potential remedy. In this paper, we propose MatchMask, a novel mask-centric generative data augmentation approach tailored for label-scarce semantic segmentation. By leveraging a limited set of labeled semantic masks, MatchMask generates diverse, realistic, and well-aligned image-mask pairs, thereby enhancing the performance of semantic segmentation models. Specifically, to adapt existing text-to-image models for semantic image synthesis in the few-shot setting, we first propose a Gradient Probe Method to investigate the role of each layer in the diffusion model. On this basis, a lightweight LoRA-style adapter is designed for critical layers to enable efficient adaptation, coupled with a Layer-adaptive Cross-attention Fusion mechanism. Meanwhile, we present a robust relative filtering principle to suppress incorrectly synthesized regions. Moreover, the proposed approach is extended to MatchMask++ in the semi-supervised setting to take advantage of additional unlabeled data. Experimental results on PASCAL VOC, COCO and ADE20K demonstrate that MatchMask remarkably enhances the performance of segmentation models, surpassing prior data augmentation techniques in various benchmarks, e.g., 67.5%->74.3% mIoU on PASCAL VOC.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:df7a595b37f59ae898361858147a9940CVF · CVPR 2026

ProSoftArena: Benchmarking Hierarchical Capabilities of Multi-modal Agents in Professional Software Environments

Ai, Jiaxin, Feng, Yukang, Zhang, Fanrui, Sun, Jianwen, Li, Zizhen, Li, Chuanhao, Chang, Yifan, Wu, Wenxiao 等 11 位作者

原文摘要

Multi-modal agents are making rapid progress on general computer-use tasks. However, existing benchmarks remain largely confined to web browsers and rudimentary applications, failing to capture the professional software workflows that dominate real-world scientific and industrial practices. To bridge this gap, we introduce ProSoftArena, a comprehensive benchmark and platform specifically designed for evaluating multi-modal agents in professional software environments. We establish the first five-level capability hierarchy for professional software manipulation, and curate a benchmark of 456 realistic tasks spanning 6 disciplines and 13 core professional applications. To ensure reliable assessment, we build an executable real-computer environment with an execution-based evaluation framework, and uniquely incorporate a human-in-the-loop evaluation paradigm to quantify agents' collaborative efficiency. Extensive experiments show that even the best-performing agent achieves only a 20.6% success rate on software-level tasks (L2) and completely fails on multi-software workflows (L3). Our in-depth analysis further provides valuable insights in current agent limitations and suggests effective design principles for building more capable agents in professional software settings. This project is available at: https://prosoftarena.github.io.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗