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Making Time Editable in Video Diffusion Transformers
Published 8 Jun 2026 in cs.CV, cs.AI, and cs.MM | (2606.10183v1)
Abstract: Modern Diffusion Transformers for video generation provide limited control over the progression of time and the editing of temporal dynamics. We propose a temporal-control methodology that extends a pretrained DiT with explicit time editing, allowing control over motion speed and temporal structure without redesigning the backbone. Its core implementation augments the pretrained model with a lightweight temporal module, preserving the original generative prior while expanding its controllable dynamic range.
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