S-HR-VQVAE: A Novel Video Prediction Model Using Hierarchical Vector Quantization and Autoregressive Spatiotemporal Modeling
This paper introduces S-HR-VQVAE, a novel video prediction model that effectively predicts future video frames by combining a hierarchical vector quantized variational autoencoder (HR-VQVAE) with an autoregressive spatiotemporal predictive model (AST-PM).
Video Prediction Under Uncertainty: A Score-Based Conditional Density Estimation Approach
This paper introduces a novel score-based framework for probabilistic video prediction that learns to sample probable future frames from a conditional density model, effectively handling occlusions and uncertainties inherent in temporal sequences.
Motion Graph: An Efficient Video Prediction Approach Using Multi-View Graph Representation for Motion Dynamics
This paper introduces motion graph, a novel motion representation for video prediction, which offers a more efficient and compact way to model complex motion dynamics compared to existing methods, leading to improved accuracy in predicting future video frames while minimizing computational resources.
EVA：一個用於未來影片預測的具身世界模型
本文介紹了一個名為 EVA 的新型具身世界模型，它能夠根據多模態指令生成未來預測影片，用於預測人類和機器人的動作，並提出了一個新的基準測試 EVA-Bench 來評估其性能。
EVA: A World Model for Anticipating Future Video Frames in Embodied Scenarios Using Multimodal Instructions
This paper introduces EVA, a novel world model framework that leverages multimodal instructions to predict future video frames in embodied scenarios, addressing the challenge of anticipating actions and generating corresponding videos in tasks involving humans and robots.
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