Making Time Editable in Video Diffusion Transformers

2026-06-08

AI Summary

This work addresses the limited controllability of temporal dynamics and editing in existing video diffusion Transformer models. To overcome this, the authors propose a lightweight temporal control module that enables explicit and fine-grained manipulation of motion speed and temporal structure without altering the pre-trained DiT backbone. By effectively leveraging the generative priors learned during pre-training, the method significantly enhances temporal controllability in video generation while preserving the original output quality. The approach thus offers a practical and efficient solution for precise temporal editing in diffusion-based video synthesis.

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.

Problem

• video generation
• temporal control
• diffusion transformers
• time editing
• motion dynamics

Innovation

• Temporal Control
• Video Diffusion Transformers
• Time Editing
• Motion Speed Manipulation
• Lightweight Temporal Module