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Egocentric video generation can be achieved by a learned transformer that probabilistically synthesizes novel views, outperforming explicit depth-based reconstruction pipelines.
Achieve video outpainting with superior temporal coherence and visual realism by unifying propagation-based and generation-based paradigms.
Unsupervised video object segmentation gets a boost from CMTM, a new method that intelligently mixes appearance and motion cues using transformers and token masking to achieve SOTA results.
