Core Insights - The CVPR 2025 conference in Nashville, Tennessee, awarded five papers, including one best paper and four honorable mentions, along with one best student paper and one honorable mention for student papers [1][2]. Submission and Acceptance Statistics - This year, over 40,000 authors submitted 13,008 papers, marking a 13% increase from last year's 11,532 submissions. A total of 2,872 papers were accepted, resulting in an overall acceptance rate of approximately 22.1%. Among the accepted papers, 96 were oral presentations (3.3%) and 387 were highlighted (13.7%) [3][5]. Conference Attendance - The conference attracted over 9,000 attendees from more than 70 countries and regions [7]. Paper Acceptance by Field - The image and video generation field had the highest number of accepted papers, while the highest acceptance rates were seen in 3D based on multi-view and sensor data, as well as single-image 3D [8]. Best Paper Award - The best paper, titled "VGGT: Visual Geometry Grounded Transformer," was presented by researchers from the University of Oxford and Meta AI. It introduced a universal 3D vision model based on a pure feedforward Transformer architecture, capable of inferring core geometric information from one or more images [13][14]. Notable Research Contributions - The best paper demonstrated significant performance improvements over traditional optimization methods and existing state-of-the-art models in various 3D tasks, achieving inference speeds in seconds without requiring post-processing optimization [17]. Best Student Paper - The best student paper, "Neural Inverse Rendering from Propagating Light," proposed a physics-based multi-view dynamic light propagation neural inverse rendering system, achieving state-of-the-art 3D reconstruction under strong indirect lighting conditions [53][55]. Awards and Recognitions - Two Young Researcher Awards were given to Hao Su and Saining Xie for their outstanding contributions to computer vision research [68][72]. The Longuet-Higgins Award was presented to two papers that have significantly influenced the field, including the Inception architecture and fully convolutional networks for semantic segmentation [75][78][80].

本文一作为肖泽琪， 本科毕业于浙江大学，现为南洋理工大学博士生， 研究方向是基于视频生成模型的世界生成和模拟，导师为潘新钢。个人主页： https://xizaoqu.github.io 近年来，基于视频生成模型的可交互世界生成引发了广泛关注。尽管现有方法在生成质量和交互能力上取得了显著进展，但由于上下文时间窗口受限，生成的世 界在长时序下严重缺乏一致性。 针对这一问题，南洋理工大学 S-Lab、北京大学与上海 AI Lab 的研究者提出了创新性的世界生成模型—— W orldM em ，通过引入记忆机制，实现了长时序一致 的世界生成。 WorldMem 在 Minecraft 数据集上进行了大规模训练，支持在多样化场景中自由探索和动态变化，并在真实数据集上验证了方法的可行性。 研究背景 世界生成模型在近期受到了广泛关注，如谷歌的 Genie 2 [1]、阿里的 The Matrix [2]、Meta 的 Navigation World Models [4] 等。这些方法在生成质量与交互性方面取 得了显著进展，但长时一致性问题仍未得到有效解决。 举例：当我们控制视角先向右转，再向左转。 在传统方法中，回看时 ...