第一人称世界模型用于逼真手-物体交互合成
Egocentric World Model for Photorealistic Hand-Object Interaction Synthesis
AI总结：
本文提出EgoHOI模型，通过动作信号模拟物理一致的交互，克服了高速头部运动、严重遮挡和高自由度手部运动带来的挑战，实验验证其有效性。
摘要：
为了作为具身AI的可扩展数据源，世界模型应作为真正的模拟器，从用户动作推断交互动态，而非依赖特权未来物体状态的条件视频生成。在此背景下，第一人称人-物体交互（HOI）世界模型对于预测物理基础的第一人称 rollout 是关键。然而，构建此类模型极具挑战性，因为快速头部运动、严重遮挡和高自由度手部运动会突然改变接触拓扑。因此，现有方法常通过依赖已知未来物体轨迹的条件视频生成来规避这些物理挑战。我们引入EgoHOI，一种第一人称HOI世界模型，摆脱这种捷径，仅通过动作信号模拟逼真且接触一致的交互。为了在不依赖未来状态输入的情况下确保物理准确性，EgoHOI从3D估计中提炼出几何和运动学先验，转化为物理感知嵌入。这些嵌入将第一人称 rollout 引向物理有效的动态。在HOT3D数据集上的实验显示，EgoHOI在强基线模型上表现一致，消融实验验证了我们物理感知设计的有效性。
英文摘要：
To serve as a scalable data source for embodied AI, world models should act as true simulators that infer interaction dynamics strictly from user actions, rather than mere conditional video generators relying on privileged future object states. In this context, egocentric Human-Object Interaction (HOI) world models are critical for predicting physically grounded first-person rollouts. However, building such models is profoundly challenging due to rapid head motions, severe occlusions, and high-DoF hand articulations that abruptly alter contact topologies. Consequently, existing approaches often circumvent these physics challenges by resorting to conditional video generation with access to known future object trajectories. We introduce EgoHOI, an egocentric HOI world model that breaks away from this shortcut to simulate photorealistic, contact-consistent interactions from action signals alone. To ensure physical accuracy without future-state inputs, EgoHOI distills geometric and kinematic priors from 3D estimates into physics-informed embeddings. These embeddings regularize the egocentric rollouts toward physically valid dynamics. Experiments on the HOT3D dataset demonstrate consistent gains over strong baselines, and ablations validate the effectiveness of our physics-informed design.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
