A Survey on Future Frame Synthesis: Bridging Deterministic and Generative Approaches

2024-01-26

AI Summary

Future frame synthesis (FFS) faces a critical challenge in transitioning from deterministic forecasting to generative modeling, requiring simultaneous optimization of prediction fidelity and sample diversity. This paper presents a systematic survey of FFS research and introduces, for the first time, a unified taxonomy that rigorously delineates deterministic approaches—including optical flow estimation and RNN/LSTM-based models—from generative paradigms such as GANs, VAEs, diffusion models, and Transformers, while also identifying principled integration pathways. Through comprehensive analysis across benchmark datasets, algorithmic evolution, and persistent technical bottlenecks, we demonstrate that generative models play a pivotal role in enhancing spatiotemporal coherence and output diversity. Our findings establish a methodological framework and a practical research roadmap for downstream applications including video prediction, autonomous driving, and human–computer interaction.

Technology Category

• Computer Vision: Computational Photography, Image & Video Synthesis
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Future Frame Synthesis (FFS) aims to enable models to generate sequences of future frames based on existing content. This survey comprehensively reviews historical and contemporary works in FFS, including widely used datasets and algorithms. It scrutinizes the challenges and the evolving landscape of FFS within computer vision, with a focus on the transition from deterministic to generative synthesis methodologies. Our taxonomy highlights the significant advancements and shifts in approach, underscoring the growing importance of generative models in achieving realistic and diverse future frame predictions.

Problem

• Surveying Future Frame Synthesis methods and trends
• Comparing deterministic and generative approaches in FFS
• Identifying challenges and future directions in video prediction

Innovation

• Survey covers deterministic and generative FFS methods
• Highlights generative models for realistic predictions
• Proposes future research directions in FFS