# Re-Attentional Controllable Video Diffusion Editing

arXiv: https://arxiv.org/abs/2412.11710 · HTML: https://mlsanity.com/paper/2412.11710/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Video generation, one of 14 directions in Diffusion models.
- Builds on 13 of its 51 references that are in the map.
- First posted on arXiv 16 Dec 2024.

## Placed in

- Diffusion models / [Video generation](https://mlsanity.com/diffusion/video-generation/)

## Builds on (most cited first, 13 of 13)

- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Imagen Video: High Definition Video Generation with Diffusion Models](https://mlsanity.com/paper/2210.02303/) (2022)
- [Pick-a-Pic: An Open Dataset of User Preferences for Text-to-Image Generation](https://mlsanity.com/paper/2305.01569/) (2023)
- [ControlVideo: Training-free Controllable Text-to-Video Generation](https://mlsanity.com/paper/2305.13077/) (2023)
- [Gen-L-Video: Multi-Text to Long Video Generation via Temporal Co-Denoising](https://mlsanity.com/paper/2305.18264/) (2023)
- [IMAGDressing-v1: Customizable Virtual Dressing](https://mlsanity.com/paper/2407.12705/) (2024)
- [Benchmarking Spatial Relationships in Text-to-Image Generation](https://mlsanity.com/paper/2212.10015/) (2022)
- [CVPR 2023 Text Guided Video Editing Competition](https://mlsanity.com/paper/2310.16003/) (2023)
- [Boosting Consistency in Story Visualization with Rich-Contextual Conditional Diffusion Models](https://mlsanity.com/paper/2407.02482/) (2024)
- [Hierarchical Spatio-temporal Decoupling for Text-to-Video Generation](https://mlsanity.com/paper/2312.04483/) (2023)
- [Control-A-Video: Controllable Text-to-Video Diffusion Models with Motion Prior and Reward Feedback Learning](https://mlsanity.com/paper/2305.13840/) (2023)
- [Paragraph-to-Image Generation with Information-Enriched Diffusion Model](https://mlsanity.com/paper/2311.14284/) (2023)
- [ControlVideo: Conditional Control for One-shot Text-driven Video Editing and Beyond](https://mlsanity.com/paper/2305.17098/) (2023)

## Built on by (most cited first, 5 of 5)

- [FlowDirector: Training-Free Flow Steering for Precise Text-to-Video Editing](https://mlsanity.com/paper/2506.05046/) (2025)
- [Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection](https://mlsanity.com/paper/2502.20981/) (2025)
- [SCMAPR: Self-Correcting Multi-Agent Prompt Refinement for Complex-Scenario Text-to-Video Generation](https://mlsanity.com/paper/2604.05489/) (2026)
- [CoT-Edit: Let CoT Guide Instruction Video Editing](https://mlsanity.com/paper/2608.01113/) (2026)
- [SimInsert: Seamless Video Object Insertion via Regional Sparse Attention Fusion](https://mlsanity.com/paper/2605.23245/) (2026)

## Abstract (arXiv)

Editing videos with textual guidance has garnered popularity due to its streamlined process which mandates users to solely edit the text prompt corresponding to the source video. Recent studies have explored and exploited large-scale text-to-image diffusion models for text-guided video editing, resulting in remarkable video editing capabilities. However, they may still suffer from some limitations such as mislocated objects, incorrect number of objects. Therefore, the controllability of video editing remains a formidable challenge. In this paper, we aim to challenge the above limitations by proposing a Re-Attentional Controllable Video Diffusion Editing (ReAtCo) method. Specially, to align the spatial placement of the target objects with the edited text prompt in a training-free manner, we propose a Re-Attentional Diffusion (RAD) to refocus the cross-attention activation responses between the edited text prompt and the target video during the denoising stage, resulting in a spatially location-aligned and semantically high-fidelity manipulated video. In particular, to faithfully preserve the invariant region content with less border artifacts, we propose an Invariant Region-guided Joint Sampling (IRJS) strategy to mitigate the intrinsic sampling errors w.r.t the invariant regions at each denoising timestep and constrain the generated content to be harmonized with the invariant region content. Experimental results verify that ReAtCo consistently improves the controllability of video diffusion editing and achieves superior video editing performance.