Investment Rating - The industry rating is "Outperform the Market" [15] Core Insights - The establishment of the Keling AI division by Kuaishou on April 30, 2025, signifies an increased focus on AI commercialization, with the division being a primary business unit alongside others like e-commerce and internationalization [3] - Keling AI has shown significant revenue growth, with a monthly revenue exceeding 10 million yuan and an annual recurring revenue (ARR) surpassing 100 million USD as of March 2025 [4] - Continuous innovation is highlighted by the introduction of the ReCamMaster video generation model, which allows users to reframe existing videos along new camera trajectories [5] Summary by Sections Industry Dynamics - Kuaishou's Keling AI division is structured with product, operations, and technology departments, reporting directly to the CEO [3] - The division's revenue trajectory indicates strong market demand and successful monetization strategies [4] Investment Recommendations - The report suggests focusing on internet companies with AI initiatives, including Kuaishou, Tencent, Alibaba, Bilibili, Meitu, and Baidu [6]

Core Viewpoint - The article introduces ReCamMaster, a video generation model that allows users to reframe existing videos along new camera trajectories, addressing common issues faced by video creators such as equipment limitations and shaky footage [2][17]. Group 1: ReCamMaster Overview - ReCamMaster enables users to upload any video and specify a new camera path for re-framing, thus enhancing the quality of video production [2]. - The model has significant applications in fields such as 4D reconstruction, video stabilization, autonomous driving, and embodied intelligence [3][17]. Group 2: Innovation and Methodology - The primary innovation of ReCamMaster lies in its new video conditioning paradigm, which combines condition video and target video in a time dimension after patchifying, resulting in substantial performance improvements over previous methods [11][17]. - The model achieves near-product-level performance in re-framing single videos, demonstrating the potential of video generation models in this area [13][17]. Group 3: MultiCamVideo Dataset - The MultiCamVideo dataset, created using Unreal Engine 5, consists of 13,600 dynamic scenes captured by 10 cameras along different trajectories, totaling 136,000 videos and 112,000 unique camera paths [13]. - The dataset features 66 different characters, 93 types of actions, and 37 high-quality 3D environments, providing a rich resource for research in camera-controlled video generation and 4D reconstruction [13][17]. Group 4: Experimental Results - ReCamMaster has shown significant performance improvements compared to baseline methods in experimental comparisons [15][17].