✨Vibe Spaces for Creatively Connecting and Expressing Visual Concepts

📝 Summary:
Vibe Blending uses Vibe Space, a hierarchical graph manifold, to create coherent and creative image hybrids. It learns geodesics in feature spaces, outperforming current methods in creativity and coherence as rated by humans.

🔹 Publication Date: Published on Dec 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.14884
• PDF: https://arxiv.org/pdf/2512.14884
• Project Page: https://huzeyann.github.io/VibeSpace-webpage/
• Github: https://github.com/huzeyann/VibeSpace

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#ImageGeneration #ComputerVision #AI #MachineLearning #CreativeAI

✨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

✨StageVAR: Stage-Aware Acceleration for Visual Autoregressive Models

📝 Summary:
StageVAR accelerates visual autoregressive models by recognizing early stages are critical while later detail-refinement stages can be pruned or approximated. This plug-and-play framework achieves up to 3.4x speedup with minimal quality loss, outperforming existing methods.

🔹 Publication Date: Published on Dec 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16483
• PDF: https://arxiv.org/pdf/2512.16483
• Github: https://github.com/sen-mao/StageVAR

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#ComputerVision #DeepLearning #ModelAcceleration #AI #NeuralNetworks

✨Both Semantics and Reconstruction Matter: Making Representation Encoders Ready for Text-to-Image Generation and Editing

📝 Summary:
This paper proposes a framework using a semantic-pixel reconstruction objective to adapt encoder features for generation. It creates a compact, semantically rich latent space, leading to state-of-the-art image reconstruction and improved text-to-image generation and editing.

🔹 Publication Date: Published on Dec 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.17909
• PDF: https://arxiv.org/pdf/2512.17909
• Project Page: https://jshilong.github.io/PS-VAE-PAGE/
• Github: https://jshilong.github.io/PS-VAE-PAGE/

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#TextToImage #ImageGeneration #DeepLearning #ComputerVision #AIResearch

✨RadarGen: Automotive Radar Point Cloud Generation from Cameras

📝 Summary:
RadarGen synthesizes realistic automotive radar point clouds from camera images using diffusion models. It incorporates depth, semantic, and motion cues for physical plausibility, enabling scalable multimodal simulation and improving perception models.

🔹 Publication Date: Published on Dec 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.17897
• PDF: https://arxiv.org/pdf/2512.17897

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#AutomotiveRadar #PointClouds #DiffusionModels #ComputerVision #AutonomousDriving

✨3D-RE-GEN: 3D Reconstruction of Indoor Scenes with a Generative Framework

📝 Summary:
3D-RE-GEN reconstructs single images into modifiable 3D textured mesh scenes with comprehensive backgrounds. It uses a compositional generative framework and novel optimization for artist-ready, physically realistic layouts, achieving state-of-the-art performance.

🔹 Publication Date: Published on Dec 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.17459
• PDF: https://arxiv.org/pdf/2512.17459
• Project Page: https://3dregen.jdihlmann.com/
• Github: https://github.com/cgtuebingen/3D-RE-GEN

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#3DReconstruction #GenerativeAI #ComputerVision #DeepLearning #ComputerGraphics

✨The Prism Hypothesis: Harmonizing Semantic and Pixel Representations via Unified Autoencoding

📝 Summary:
The Prism Hypothesis posits semantic encoders capture low-frequency meaning, while pixel encoders retain high-frequency details. Unified Autoencoding UAE leverages this with a frequency-band modulator to harmonize both into a single latent space. This achieves state-of-the-art performance on imag...

🔹 Publication Date: Published on Dec 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.19693
• PDF: https://arxiv.org/pdf/2512.19693
• Github: https://github.com/WeichenFan/UAE

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#DeepLearning #ComputerVision #Autoencoders #RepresentationLearning #AIResearch

✨LongVideoAgent: Multi-Agent Reasoning with Long Videos

📝 Summary:
A multi-agent framework with a master LLM, grounding agent, and vision agent enhances long-video QA by improving temporal grounding and extracting visual details. This RL-trained system outperforms non-agent baselines on new datasets.

🔹 Publication Date: Published on Dec 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.20618
• PDF: https://arxiv.org/pdf/2512.20618
• Github: https://longvideoagent.github.io/

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#MultiAgentSystems #LLM #VideoUnderstanding #ComputerVision #AI

✨Learning to Refocus with Video Diffusion Models

📝 Summary:
A novel method enables realistic post-capture refocusing from a single defocused image. It uses video diffusion models to generate a focal stack for interactive focus adjustment. This approach outperforms existing methods, improving photography focus-editing.

🔹 Publication Date: Published on Dec 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.19823
• PDF: https://arxiv.org/pdf/2512.19823
• Project Page: https://learn2refocus.github.io/
• Github: https://github.com/tedlasai/learn2refocus

🔹 Models citing this paper:
• https://huggingface.co/tedlasai/learn2refocus

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#VideoDiffusionModels #ComputationalPhotography #ImageRefocusing #DeepLearning #ComputerVision

✨Learning to Reason in 4D: Dynamic Spatial Understanding for Vision Language Models

📝 Summary:
DSR Suite improves vision language models weak dynamic spatial reasoning. It creates 4D training data from videos using an automated pipeline and integrates geometric priors via a Geometry Selection Module. This significantly enhances VLM dynamic spatial reasoning capability while maintaining gen...

🔹 Publication Date: Published on Dec 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.20557
• PDF: https://arxiv.org/pdf/2512.20557

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#VisionLanguageModels #SpatialReasoning #4D #ComputerVision #AIResearch

✨Latent Implicit Visual Reasoning

📝 Summary:
Large Multimodal Models struggle with visual reasoning due to their text-centric nature and limitations of prior methods. This paper introduces a task-agnostic mechanism for LMMs to discover and use visual reasoning tokens without explicit supervision. The approach achieves state-of-the-art resul...

🔹 Publication Date: Published on Dec 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.21218
• PDF: https://arxiv.org/pdf/2512.21218

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#LMMs #VisualReasoning #AI #ComputerVision #DeepLearning

✨Spatia: Video Generation with Updatable Spatial Memory

📝 Summary:
Spatia is a video generation framework that improves long-term consistency by using an updatable 3D scene point cloud as persistent spatial memory. It iteratively generates video clips and updates this memory via visual SLAM, enabling realistic videos and 3D-aware interactive editing.

🔹 Publication Date: Published on Dec 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.15716
• PDF: https://arxiv.org/pdf/2512.15716
• Project Page: https://zhaojingjing713.github.io/Spatia/
• Github: https://github.com/ZhaoJingjing713/Spatia

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#VideoGeneration #GenerativeAI #ComputerVision #3DReconstruction #SLAM

✨How Much 3D Do Video Foundation Models Encode?

📝 Summary:
A new framework quantifies 3D understanding in Video Foundation Models VidFMs. VidFMs, trained only on video, show strong 3D awareness, often surpassing expert 3D models, providing insights for 3D AI.

🔹 Publication Date: Published on Dec 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.19949
• PDF: https://arxiv.org/pdf/2512.19949
• Project Page: https://vidfm-3d-probe.github.io/
• Github: https://vidfm-3d-probe.github.io

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#VideoFoundationModels #3DUnderstanding #ComputerVision #AIResearch #DeepLearning

✨Fast3R: Towards 3D Reconstruction of 1000+ Images in One Forward Pass

📝 Summary:
Fast3R is a Transformer-based method for efficient and scalable multi-view 3D reconstruction. It processes many images in parallel in a single forward pass, improving speed and accuracy over pairwise approaches like DUSt3R.

🔹 Publication Date: Published on Jan 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2501.13928
• PDF: https://arxiv.org/pdf/2501.13928
• Github: https://github.com/naver/dust3r/pull/16

🔹 Models citing this paper:
• https://huggingface.co/jedyang97/Fast3R_ViT_Large_512

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#3DReconstruction #ComputerVision #Transformers #Fast3R #DeepLearning