✨ReViSE: Towards Reason-Informed Video Editing in Unified Models with Self-Reflective Learning

📝 Summary:
The ReViSE framework enables reason-informed video editing by addressing the disconnect between models reasoning and editing capabilities. It uses a self-reflective learning mechanism with an internal VLM to provide intrinsic feedback. This significantly enhances editing accuracy and visual fidel...

🔹 Publication Date: Published on Dec 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.09924
• PDF: https://arxiv.org/pdf/2512.09924
• Github: https://github.com/Liuxinyv/ReViSE

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#VideoEditing #AI #MachineLearning #VLM #SelfReflectiveLearning

✨V-RGBX: Video Editing with Accurate Controls over Intrinsic Properties

📝 Summary:
V-RGBX is an end-to-end framework for intrinsic-aware video editing. It combines video inverse rendering with photorealistic synthesis and keyframe editing of intrinsic properties. This allows consistent, physically plausible video manipulation, like relighting or object appearance changes.

🔹 Publication Date: Published on Dec 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.11799
• PDF: https://arxiv.org/pdf/2512.11799
• Project Page: https://aleafy.github.io/vrgbx/
• Github: https://github.com/Aleafy/V-RGBX

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#VideoEditing #ComputerVision #InverseRendering #NeuralRendering #Graphics

✨EasyV2V: A High-quality Instruction-based Video Editing Framework

📝 Summary:
EasyV2V is a framework for instruction-based video editing that combines diverse data sources, leverages pretrained text-to-video models with LoRA fine-tuning, and uses unified spatiotemporal control. This innovative approach achieves state-of-the-art results in video editing.

🔹 Publication Date: Published on Dec 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16920
• PDF: https://arxiv.org/pdf/2512.16920
• Github: https://snap-research.github.io/easyv2v/

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#VideoEditing #AI #DeepLearning #ComputerVision #TextToVideo

✨InsertAnywhere: Bridging 4D Scene Geometry and Diffusion Models for Realistic Video Object Insertion

📝 Summary:
InsertAnywhere is a framework for realistic video object insertion. It uses 4D aware mask generation for geometric consistency and an extended diffusion model for appearance-faithful synthesis, outperforming existing methods.

🔹 Publication Date: Published on Dec 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.17504
• PDF: https://arxiv.org/pdf/2512.17504
• Project Page: https://myyzzzoooo.github.io/InsertAnywhere/
• Github: https://github.com/myyzzzoooo/InsertAnywhere

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#VideoEditing #DiffusionModels #ComputerVision #DeepLearning #GenerativeAI

✨Memory-V2V: Augmenting Video-to-Video Diffusion Models with Memory

📝 Summary:
Memory-V2V enhances multi-turn video editing by adding explicit memory to diffusion models. It ensures cross-consistency using efficient token compression and retrieval. This significantly improves video consistency and performance with low computational cost.

🔹 Publication Date: Published on Jan 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.16296
• PDF: https://arxiv.org/pdf/2601.16296
• Project Page: https://dohunlee1.github.io/MemoryV2V

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#VideoEditing #DiffusionModels #GenerativeAI #ComputerVision #MachineLearning

✨EffectErase: Joint Video Object Removal and Insertion for High-Quality Effect Erasing

📝 Summary:
EffectErase is a new video object removal method that effectively erases dynamic objects and their visual effects. It introduces VOR, a large dataset for training, and uses reciprocal learning with task-aware guidance for high-quality results.

🔹 Publication Date: Published on Mar 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.19224
• PDF: https://arxiv.org/pdf/2603.19224
• Project Page: https://henghuiding.com/EffectErase/
• Github: https://github.com/FudanCVL/EffectErase

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#VideoEditing #ComputerVision #ObjectRemoval #DeepLearning #AI

✨Versatile Editing of Video Content, Actions, and Dynamics without Training

📝 Summary:
DynaEdit is a training-free method for versatile video editing using pretrained text-to-video models. It addresses limitations in handling complex edits, actions, and object interactions by solving technical issues like misalignment and jitter, achieving state-of-the-art results.

🔹 Publication Date: Published on Mar 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.17989
• PDF: https://arxiv.org/pdf/2603.17989
• Project Page: https://dynaedit.github.io

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#VideoEditing #TextToVideo #GenerativeAI #ComputerVision #AIResearch

✨VOID: Video Object and Interaction Deletion

📝 Summary:
VOID is a video object removal framework designed for complex scenarios involving significant object interactions. It uses vision-language and video diffusion models, leveraging causal reasoning to generate physically plausible counterfactual scenes. VOID better preserves consistent scene dynamic...

🔹 Publication Date: Published on Apr 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.02296
• PDF: https://arxiv.org/pdf/2604.02296
• Project Page: https://void-model.github.io/
• Github: https://github.com/Netflix/void-model

🔹 Models citing this paper:
• https://huggingface.co/netflix/void-model

✨ Spaces citing this paper:
• https://huggingface.co/spaces/sam-motamed/VOID

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#VideoEditing #DiffusionModels #ComputerVision #GenerativeAI #DeepLearning

✨VEFX-Bench: A Holistic Benchmark for Generic Video Editing and Visual Effects

📝 Summary:
VEFX-Bench offers a large human-annotated video editing dataset and VEFX-Reward, a specialized model for quality assessment. This benchmark allows standardized comparison, showing current models struggle with instruction following and edit locality.

🔹 Publication Date: Published on Apr 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.16272
• PDF: https://arxiv.org/pdf/2604.16272
• Project Page: https://xiangbogaobarry.github.io/VEFX-Bench/

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#VideoEditing #VFX #AI #ComputerVision #Benchmarks

✨FlowAnchor: Stabilizing the Editing Signal for Inversion-Free Video Editing

📝 Summary:
FlowAnchor stabilizes inversion-free video editing by addressing signal instability in high-dimensional latent spaces. It uses spatial-aware attention refinement and adaptive magnitude modulation to ensure precise localization and sufficient editing strength, leading to faithful and coherent vide...

🔹 Publication Date: Published on Apr 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.22586
• PDF: https://arxiv.org/pdf/2604.22586

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#VideoEditing #DeepLearning #ComputerVision #GenerativeAI #AIResearch