Video Analysis and Generation via a Semantic Progress Function

About this episode

The episode discusses a new framework for analyzing and generating video content using a Semantic Progress Function to ensure smoother transitions.

🤗 Upvotes: 42 | cs.CV Authors: Gal Metzer, Sagi Polaczek, Ali Mahdavi-Amiri, Raja Giryes, Daniel Cohen-Or Title: Video Analysis and Generation via a Semantic Progress Function Arxiv: http://arxiv.org/abs/2604.22554v1 Abstract: Transformations produced by image and video generation models often evolve in a highly non-linear manner: long stretches where the content barely changes are followed by sudden, abrupt semantic jumps. To analyze and correct this behavior, we introduce a Semantic Progress Function, a one-dimensional representation that captures how the meaning of a given sequence evolves over time. For each frame, we compute distances between semantic embeddings and fit a smooth curve that reflects the cumulative semantic shift across the sequence. Departures of this curve from a straight line reveal uneven semantic pacing. Building on this insight, we propose a semantic linearization procedure that reparameterizes (or retimes) the sequence so that semantic change unfolds at a constant rate, yielding smoother and more coherent transitions. Beyond linearization, our framework provides a model-agnostic foundation for identifying temporal irregularities, comparing semantic…

People in this episode

Hosts: Jingwen Liang, Gengyu Wang

Topics covered

• video analysis
• semantic progress function
• video generation
• semantic pacing
• non-linear transformations

Keywords

• video analysis
• semantic progress function
• video generation
• semantic pacing
• non-linear transformations

Mentioned in this episode

Books & works: Video Analysis and Generation via a Semantic Progress Function

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