PhysChoreo: 具有部分感知语义基础的物理可控视频生成
PhysChoreo: Physics-Controllable Video Generation with Part-Aware Semantic Grounding
AI总结：
提出PhysChoreo框架，通过两阶段方法（部分感知物理属性重建与时序指导的可编辑物理模拟）从单张图像生成物理可控且逼真的视频，在多个指标上超越现有方法。
摘要：
尽管最近的视频生成模型在视觉保真度上取得了显著成果，但它们往往缺乏明确的物理可控性和合理性。为了解决这个问题，一些近期研究尝试用基于物理的渲染来引导视频生成。然而，这些方法在准确建模复杂物理属性和有效控制长时间序列上的物理行为方面面临固有挑战。在这项工作中，我们提出了PhysChoreo，一个新颖的框架，能够从单张图像生成具有多样可控性和物理真实感的视频。我们的方法包括两个阶段：首先，通过部分感知的物理属性重建来估计图像中所有物体的静态初始物理属性。然后，通过时序指导且可编辑的物理模拟，合成具有丰富动态行为和物理真实感的高质量视频。实验结果表明，PhysChoreo能够生成具有丰富行为和物理真实感的视频，在多个评估指标上优于最先进的方法。
英文摘要：
While recent video generation models have achieved significant visual fidelity, they often suffer from the lack of explicit physical controllability and plausibility. To address this, some recent studies attempted to guide the video generation with physics-based rendering. However, these methods face inherent challenges in accurately modeling complex physical properties and effectively control ling the resulting physical behavior over extended temporal sequences. In this work, we introduce PhysChoreo, a novel framework that can generate videos with diverse controllability and physical realism from a single image. Our method consists of two stages: first, it estimates the static initial physical properties of all objects in the image through part-aware physical property reconstruction. Then, through temporally instructed and physically editable simulation, it synthesizes high-quality videos with rich dynamic behaviors and physical realism. Experimental results show that PhysChoreo can generate videos with rich behaviors and physical realism, outperforming state-of-the-art methods on multiple evaluation metrics.
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