arXiv Computer Vision
TripleFlow: Training-Free Video Object Removal by Bridging Residual Editing and Native Generation
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arXiv Computer Vision
Aug 21
BeyondMasks: Evaluating Causal and Physical Consistency in Video Object Removal
arXiv:2608. 20107v1 Announce Type: new Abstract: Recent advances in generative video models have significantly improved visual realism in video object removal, yet evaluation protocols still focus on masked region fidelity, treating removal as local inpainting.
arXiv Computer Vision
Aug 25
FixAnything: 3D-Consistent Rendering Refinement via Video Generative Priors
arXiv:2608.23549v1 Announce Type: new Abstract: Rendering views using 3D scene representations such as Gaussian Splatting (3DGS), Neural Radiance Fields (NeRF), meshes, or even point clouds produces...
arXiv AI
2d ago
MoCA-Video: Motion-Aware Concept Alignment for Consistent Video Editing
arXiv:2506.01004v3 Announce Type: replace-cross Abstract: Unlike traditional video editing or inpainting, video semantic mixing fuses a reference concept with a moving target entity to produce a hybr...
Hugging Face Trending Papers
Aug 24
FixAnything: 3D-Consistent Rendering Refinement via Video Generative Priors
Rendering views using 3D scene representations such as Gaussian Splatting (3DGS), Neural Radiance Fields (NeRF), meshes, or even point clouds produces artifacts when input views are sparse or target v...
arXiv Computer Vision
Aug 27
FlowMimic: Mask-free Visual Editing and Generation with Pixel-pair Warped Flow Field for Online Video Editing Data Generation and Modality Mimicry
arXiv:2607.18227v2 Announce Type: replace Abstract: In line with the prevailing direction of vision research, we explore the integration of both generation and editing capabilities for video and imag...
arXiv Computer Vision
1d ago
FOMO: Forget the Concept, Don't Miss Out on the Scene in Selective Video Unlearning
FOMO is a training‑based selective video unlearning method that prioritizes preserving the original scene while removing targeted concepts. It localizes concept‑related representations for modification and employs a preservation mechanism that maintains non‑target scene information without auxiliary data. The approach extends to motion unlearning, enabling removal of concepts defined by temporal behavior, and achieves a strong balance between concept removal and scene preservation.