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Temporally Consistent Transformers for Video Generation
Wilson Yan · Danijar Hafner · Stephen James · Pieter Abbeel

Wed Jul 26 02:00 PM -- 03:30 PM (PDT) @ Exhibit Hall 1 #619

To generate accurate videos, algorithms have to understand the spatial and temporal dependencies in the world. Current algorithms enable accurate predictions over short horizons but tend to suffer from temporal inconsistencies. When generated content goes out of view and is later revisited, the model invents different content instead. Despite this severe limitation, no established benchmarks exist for video generation with long temporal dependencies. In this paper, we curate 3 challenging video datasets with long-range dependencies by rendering walks through 3D scenes of procedural mazes, Minecraft worlds, and indoor scans. We perform a comprehensive evaluation of current models and observe their limitations in temporal consistency. Moreover, we introduce the Temporally Consistent Transformer (TECO), a generative model that substantially improves long-term consistency while also reducing sampling time. By compressing its input sequence into fewer embeddings, applying a temporal transformer, and expanding back using a spatial MaskGit, TECO outperforms existing models across many metrics. Videos are available on the website: https://wilson1yan.github.io/teco

Author Information

Wilson Yan (UC Berkeley)
Danijar Hafner (DeepMind)
Stephen James (Dyson)
Pieter Abbeel (UC Berkeley & Covariant)

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