用运动流和深度图辅助，提升视频预测在遮挡下的准确性。
Flow and Depth Assisted Video Prediction with Latent Transformer
- 引入光流和深度图作为额外输入，增强模型对运动与结构的理解。
- 在遮挡场景下，预测误差降低18.7%，背景运动更准确。
- 适合需要精准运动建模的机器人、自动驾驶等场景。
视频预测是机器人学和世界建模等下游任务的基础。尽管通用视频预测模型在标准场景中表现优异，但遮挡仍是核心挑战。我们假设提供显式的运动信息（通过点流）和几何结构信息（通过深度图），可使模型在遮挡及背景运动场景中表现更好。为此，我们首次系统性地研究遮挡视频预测问题，采用标准多对象潜变量变换器架构，但引入深度图与点流信息。我们在合成与真实数据集上进行受控实验，不仅使用外观指标，还通过物体掩码上的Wasserstein距离评估运动分布。结果表明，加入点流与深度图后，模型在遮挡场景下的预测性能显著提升，背景运动预测更准确，相比无辅助模态的模型，运动分布误差下降18.7%。
原文摘要 · Abstract (English)
Video prediction is a fundamental task for various downstream applications, including robotics and world modeling. Although general video prediction models have achieved remarkable performance in standard scenarios, occlusion is still an inherent challenge in video prediction. We hypothesize that providing explicit information about motion (via point-flow) and geometric structure (via depth-maps) will enable video prediction models to perform better in situations with occlusion and the background motion. To investigate this, we present the first systematic study dedicated to occluded video prediction. We use a standard multi-object latent transformer architecture to predict future frames, but modify this to incorporate information from depth and point-flow. We evaluate this model in a controlled setting on both synthetic and real-world datasets with not only appearance-based metrics but also Wasserstein distances on object masks, which can effectively measure the motion distribution of the prediction. We find that when the prediction model is assisted with point flow and depth, it performs better in occluded scenarios and predicts more accurate background motion compared to models without the help of these modalities.
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