TripleFlow: Training-Free Video Object Removal by Bridging Residual Editing and Native Generation

arXiv:2609.39157 · cs.CV · Submitted 2026-09-30 · Read on arXiv

cs.CV

Submitted: 2026-09-30

Updated: 2026-10-05

Code: https://github.com/IDEA-Research/Grounded-SAM-2

Terminology

• Core — 14× in this paper · explained in 2 episodes
• Benchmark — 13× in this paper · explained in 2 episodes
• Ablation — 8× in this paper · explained in 1 episode
• Diffusion Model — 7× in this paper · explained in 8 episodes
• Transformer — 7× in this paper · explained in 2 episodes
• Diffusion Models — 6× in this paper · explained in 6 episodes
• Consistency — 4× in this paper · explained in 3 episodes
• Aggregation — 3× in this paper · explained in 1 episode
• Computer Vision and Pattern Recognition — 3× in this paper
• Fidelity — 3× in this paper · explained in 2 episodes
• Rectified Flow — 3× in this paper · explained in 3 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• Alignment — 2× in this paper · explained in 1 episode
• Generative Model — 2× in this paper · explained in 2 episodes
• Generative Models — 2× in this paper · explained in 2 episodes
• Temporal consistency — 2× in this paper · explained in 2 episodes
• Training-Free Framework — 2× in this paper · explained in 2 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• CVF International Conference on Computer Vision — 1× in this paper
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Flow Matching — 1× in this paper · explained in 8 episodes
• Frozen model — 1× in this paper · explained in 3 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Guided attention — 1× in this paper · explained in 2 episodes
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• Optical Flow — 1× in this paper · explained in 2 episodes
• Steering — 1× in this paper · explained in 2 episodes

Sources

• SAM 3: Segment Anything with Concepts
• FLATTEN: optical FLow-guided ATTENtion for consistent text-to-video editing
• EffectErase: Joint Video Object Removal and Insertion for High-Quality Effect Erasing
• TokenFlow: Consistent Diffusion Features for Consistent Video Editing
• Prompt-to-Prompt Image Editing with Cross Attention Control
• Video Diffusion Models
• From Ideal to Real: Stable Video Object Removal under Imperfect Conditions
• FlowEdit: Inversion-Free Text-Based Editing Using Pre-Trained Flow Models
• Object-WIPER : Training-Free Object and Associated Effect Removal in Videos
• PROVE: A Perceptual RemOVal cohErence Benchmark for Visual Media
• FlowDirector: Training-Free Flow Steering for Precise Text-to-Video Editing
• DiffuEraser: A Diffusion Model for Video Inpainting
• Flow Matching for Generative Modeling
• Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow
• ROSE: Remove Objects with Side Effects in Videos
• The 2017 DAVIS Challenge on Video Object Segmentation
• SAM 2: Segment Anything in Images and Videos
• OmnimatteZero: Fast Training-free Omnimatte with Pre-trained Video Diffusion Models
• Wan: Open and Advanced Large-Scale Video Generative Models

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