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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2604.22554 |
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| _version_ | 1866908991085346816 |
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| author | Metzer, Gal Polaczek, Sagi Mahdavi-Amiri, Ali Giryes, Raja Cohen-Or, Daniel |
| author_facet | Metzer, Gal Polaczek, Sagi Mahdavi-Amiri, Ali Giryes, Raja Cohen-Or, Daniel |
| contents | 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 pacing across different generators, and steering both generated and real-world video sequences toward arbitrary target pacing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_22554 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Video Analysis and Generation via a Semantic Progress Function Metzer, Gal Polaczek, Sagi Mahdavi-Amiri, Ali Giryes, Raja Cohen-Or, Daniel Computer Vision and Pattern Recognition 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 pacing across different generators, and steering both generated and real-world video sequences toward arbitrary target pacing. |
| title | Video Analysis and Generation via a Semantic Progress Function |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.22554 |