ContextFlow: Training-Free Video Object Editing via Adaptive Context
  Enrichment
Authors: name: Yiyang Chen, name: Xuanhua He, name: Xiujun Ma, name: Yue Ma

Training-free video object editing aims to achieve precise object-level
manipulation, including object insertion, swapping, and deletion. However, it
faces significant challenges in maintaining fidelity and temporal consistency.
Existing methods, often designed for U-Net architectures, suffer from two
primary limitations: inaccurate inversion due to first-order solvers, and
contextual conflicts caused by crude "hard" feature replacement. These issues
are more challenging in Diffusion Transformers (DiTs), where the unsuitability
of prior layer-selection heuristics makes effective guidance challenging. To
address these limitations, we introduce ContextFlow, a novel training-free
framework for DiT-based video object editing. In detail, we first employ a
high-order Rectified Flow solver to establish a robust editing foundation. The
core of our framework is Adaptive Context Enrichment (for specifying what to
edit), a mechanism that addresses contextual conflicts. Instead of replacing
features, it enriches the self-attention context by concatenating Key-Value
pairs from parallel reconstruction and editing paths, empowering the model to
dynamically fuse information. Additionally, to determine where to apply this
enrichment (for specifying where to edit), we propose a systematic, data-driven
analysis to identify task-specific vital layers. Based on a novel Guidance
Responsiveness Metric, our method pinpoints the most influential DiT blocks for
different tasks (e.g., insertion, swapping), enabling targeted and highly
effective guidance. Extensive experiments show that ContextFlow significantly
outperforms existing training-free methods and even surpasses several
state-of-the-art training-based approaches, delivering temporally coherent,
high-fidelity results.