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WanderBench:世界模型能否从视频中探索动态世界?

原文标题 · WanderBench: Can World Models Explore Dynamic Worlds from Video?

作者: 陈宇航、李继恒、王嘉乐 | 基于输入视频条件的视频世界模型使得人们能够探索真实、已录制的动态场景,从而实现素材重拍摄、自由视角回放以及用于具身智能系统的真实至仿真环境。此类应用要求生成的滚动展开结果对已录制世界保持忠实,然而现有基准测试起始于单张图像或文本提示,而视频条件生成则属于

来源:arXiv cs.AI/CL/LG · arxiv.org

正文为英文,可一键机器翻译(仅首次需要等待)

Computer Science > Computer Vision and Pattern Recognition

Title: WanderBench: Can World Models Explore Dynamic Worlds from Video?

Abstract: Video world models conditioned on an input video make it possible to explore real, recorded dynamic scenes, enabling re-shooting of footage, free-viewpoint replay, and real-to-sim environments for embodied agents. Such uses require the generated rollout to stay faithful to the recorded world, yet existing benchmarks start from a single image or a text prompt, and video-conditioned generation is evaluated mainly on perceptual quality and camera following. These evaluations do not directly assess whether exploration preserves subject identity, event progression, and scene consistency upon revisiting a location. We introduce WanderBench, a benchmark for exploring dynamic worlds from video. It comprises 745 single-shot source videos of real and AI-generated content in three difficulty tiers (3-25 s), paired with camera routes given as keyboard actions, and evaluates six dimensions with 24 human-validated sub-metrics. Benchmarking eight models, we find that as clips grow longer, models keep the static scene and visual quality but lose camera control and the source dynamics, and that none keeps revisited places consistent. We further propose WanderLand, a training-free framework that conditions each frame of a frozen model on the same-instant source and, on revisits, on its own earlier static content. WanderLand improves five models on every dimension, by up to 8.3 points in source-dynamics fidelity.

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