Making Time Editable in Video Diffusion Transformers
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Video GenerationSimilar Papers 제목 키워드 기반
LynnReal-Omni: Native multi-modal Video Generation for Agentic Visual Workflows
Video diffusion models are stochastic and hard to control: precise content often requires repeated sampling without guaranteed success, and long-horizon scenes drift in appearance, interactions, and temporal coherence. A…
Instruction FollowingVideo RestorationVideo GenerationMultiGen: Level-Design for Editable Multiplayer Worlds in Diffusion Game Engines
Video world models have shown immense promise for interactive simulation and entertainment, but current systems still struggle with two important aspects of interactivity: user control over the environment for reproducib…
OneTo3D: One Image to Re-editable Dynamic 3D Model and Video Generation
One image to editable dynamic 3D model and video generation is novel direction and change in the research area of single image to 3D representation or 3D reconstruction of image. Gaussian Splatting has demonstrated its a…
3D ReconstructionImage to 3DVideo GenerationInstantViR: Real-Time Video Inverse Problem Solver with Distilled Diffusion Prior
Video inverse problems are fundamental to streaming, telepresence, and AR/VR, where high perceptual quality must coexist with tight latency constraints. Diffusion-based priors currently deliver state-of-the-art reconstru…
Video ReconstructionVideo RestorationLighting-grounded Video Generation with Renderer-based Agent Reasoning
Diffusion models have achieved remarkable progress in video generation, but their controllability remains a major limitation. Key scene factors such as layout, lighting, and camera trajectory are often entangled or only …
Video-to-Video SynthesisVideo Generation