无需训练的世界模型？西湖大学WorldForge开启空间智能新路径，让AI读懂3D世界

Core Viewpoint - The article discusses the advancements in AI-generated video content, highlighting the challenges of controllability in video generation models and introducing WorldForge as a solution to enhance precision in video creation without altering the model's weights [1][2]. Group 1: Challenges in Video Generation - AI-generated videos have gained significant attention due to their realistic visuals, but the lack of precise control over generated content remains a major limitation [1]. - Current models often require extensive retraining to improve controllability, which can be costly in terms of time and computational resources, potentially degrading the model's generalization ability [1]. Group 2: Introduction of WorldForge - WorldForge offers an innovative approach by guiding existing video generation models during the inference phase, allowing for precise control without modifying the model's weights [2][14]. - The framework consists of three collaborative modules designed to enhance the generation process [4]. Group 3: Key Modules of WorldForge - Intra-step Recursive Refinement (IRR): This module sets boundaries for the AI's imagination by implementing a "predict-correct" micro-loop, allowing for timely corrections after each prediction to ensure adherence to a predefined trajectory [4][5]. - Flow-Gated Latent Fusion (FLF): This module separates appearance and motion features, injecting motion signals only into relevant channels to maintain the quality of the generated content while controlling the perspective [6][7]. - Dual-Path Self-Correcting Guidance (DSG): DSG addresses the imperfections in injected guidance signals by utilizing two parallel denoising paths to ensure high-quality output while adhering to trajectory constraints [7]. Group 4: Applications of WorldForge - WorldForge demonstrates remarkable capabilities, such as reconstructing 3D static scenes from a single image and generating 360° surround videos, indicating its potential for efficient world model exploration [9][8]. - The system allows users to design new camera trajectories for existing videos, executing complex movements and intelligently filling in newly exposed areas, outperforming traditional models that require extensive training [11]. - Additionally, WorldForge supports video content editing, including subject replacement and object manipulation, enabling creative modifications [12]. Group 5: Future Implications - WorldForge introduces a novel interactive and control approach in video generation, paving the way for the development of controllable world models without increasing training costs or losing prior knowledge [14]. - The potential for future advancements includes more natural interactions through language or gestures, allowing models to better understand and execute creative visions [14].