✨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

✨MineTheGap: Automatic Mining of Biases in Text-to-Image Models

📝 Summary:
MineTheGap automatically finds prompts that cause Text-to-Image models to generate biased outputs. It uses a genetic algorithm and a novel bias score to identify and rank biases, aiming to reduce redundancy and improve output diversity.

🔹 Publication Date: Published on Dec 15

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.13427
• PDF: https://arxiv.org/pdf/2512.13427

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#AIbias #TextToImage #GenerativeAI #ResponsibleAI #MachineLearning

✨Over++: Generative Video Compositing for Layer Interaction Effects

📝 Summary:
Over++ introduces augmented compositing, a framework that generates realistic, text-prompted environmental effects for videos. It synthesizes effects like shadows onto video layers while preserving the original scene, outperforming prior methods without dense annotations.

🔹 Publication Date: Published on Dec 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.19661
• PDF: https://arxiv.org/pdf/2512.19661
• Project Page: https://overplusplus.github.io/

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#GenerativeAI #VideoCompositing #VFX #ComputerGraphics #AIResearch

✨T2AV-Compass: Towards Unified Evaluation for Text-to-Audio-Video Generation

📝 Summary:
T2AV-Compass introduces a unified benchmark for text-to-audio-video generation evaluation. It features 500 diverse prompts and a dual-level framework. Evaluations reveal current T2AV models struggle significantly with realism and cross-modal consistency.

🔹 Publication Date: Published on Dec 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.21094
• PDF: https://arxiv.org/pdf/2512.21094
• Project Page: https://nju-link.github.io/T2AV-Compass/
• Github: https://github.com/NJU-LINK/T2AV-Compass/

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#TextToAudioVideo #MultimodalAI #AIEvaluation #GenerativeAI #AIResearch

✨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

✨SkyReels-V2: Infinite-length Film Generative Model

📝 Summary:
SkyReels-V2 is an infinite-length film generative model that addresses video generation challenges by synergizing MLLMs, reinforcement learning, and a diffusion forcing framework. It enables high-quality, long-form video synthesis with realistic motion and cinematic grammar awareness through mult...

🔹 Publication Date: Published on Apr 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2504.13074
• PDF: https://arxiv.org/pdf/2504.13074
• Github: https://github.com/skyworkai/skyreels-v2

🔹 Models citing this paper:
• https://huggingface.co/Skywork/SkyReels-V2-I2V-14B-540P
• https://huggingface.co/Skywork/SkyCaptioner-V1
• https://huggingface.co/Skywork/SkyReels-V2-I2V-1.3B-540P

✨ Spaces citing this paper:
• https://huggingface.co/spaces/fffiloni/SkyReels-V2
• https://huggingface.co/spaces/Dudu0043/SkyReels-V2
• https://huggingface.co/spaces/14eee109giet/SkyReels-V2

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#VideoGeneration #GenerativeAI #MLLM #DiffusionModels #AIResearch

✨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

✨Mindscape-Aware Retrieval Augmented Generation for Improved Long Context Understanding

📝 Summary:
MiA-RAG enhances RAG systems with global context awareness, inspired by human understanding. It uses hierarchical summarization to build a 'mindscape,' improving long-context retrieval and generation for better evidence-based understanding.

🔹 Publication Date: Published on Dec 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.17220
• PDF: https://arxiv.org/pdf/2512.17220

🔹 Models citing this paper:
• https://huggingface.co/MindscapeRAG/MiA-Emb-8B
• https://huggingface.co/MindscapeRAG/MiA-Emb-4B
• https://huggingface.co/MindscapeRAG/MiA-Emb-0.6B

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#RAG #LLM #NLP #GenerativeAI #ContextUnderstanding

✨Yume-1.5: A Text-Controlled Interactive World Generation Model

📝 Summary:
Yume-1.5 is a novel framework that generates realistic, interactive, and continuous worlds from a single image or text prompt. It overcomes prior limitations in real-time performance and text control by using unified context compression, streaming acceleration, and text-controlled world events.

🔹 Publication Date: Published on Dec 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.22096
• PDF: https://arxiv.org/pdf/2512.22096
• Project Page: https://stdstu12.github.io/YUME-Project/
• Github: https://github.com/stdstu12/YUME

🔹 Models citing this paper:
• https://huggingface.co/stdstu123/Yume-5B-720P

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#AI #GenerativeAI #WorldGeneration #ComputerGraphics #DeepLearning

✨UltraShape 1.0: High-Fidelity 3D Shape Generation via Scalable Geometric Refinement

📝 Summary:
UltraShape 1.0 is a 3D diffusion framework that generates high-fidelity shapes using a two-stage process: coarse then refined geometry. It includes a novel data pipeline improving dataset quality, enabling strong geometric results on public data.

🔹 Publication Date: Published on Dec 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.21185
• PDF: https://arxiv.org/pdf/2512.21185
• Project Page: https://pku-yuangroup.github.io/UltraShape-1.0/
• Github: https://pku-yuangroup.github.io/UltraShape-1.0/

🔹 Models citing this paper:
• https://huggingface.co/infinith/UltraShape

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#3DGeneration #DiffusionModels #GenerativeAI #ComputerGraphics #DeepLearning