✨DraCo: Draft as CoT for Text-to-Image Preview and Rare Concept Generation

📝 Summary:
DraCo is a novel text-to-image generation method that uses interleaved reasoning with both textual and visual content. It generates low-resolution drafts, verifies semantic alignment, and refines images to address coarse textual planning and rare attribute generation. DraCo significantly outperfo...

🔹 Publication Date: Published on Dec 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.05112
• PDF: https://arxiv.org/pdf/2512.05112
• Github: https://github.com/CaraJ7/DraCo

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#TextToImage #GenerativeAI #DeepLearning #ComputerVision #AI

✨BulletTime: Decoupled Control of Time and Camera Pose for Video Generation

📝 Summary:
This paper presents a video diffusion framework that decouples scene dynamics from camera pose. This enables precise 4D control over time and viewpoint for high-quality video generation, outperforming prior models in controllability.

🔹 Publication Date: Published on Dec 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.05076
• PDF: https://arxiv.org/pdf/2512.05076

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#VideoGeneration #DiffusionModels #GenerativeAI #ComputerVision #AICameraControl

✨Live Avatar: Streaming Real-time Audio-Driven Avatar Generation with Infinite Length

📝 Summary:
Live Avatar uses a 14-billion-parameter diffusion model to achieve real-time, high-fidelity, infinite-length audio-driven avatar generation. It employs Timestep-forcing Pipeline Parallelism and Rolling Sink Frame Mechanism for efficiency and consistency, reaching 20 FPS on 5 H800 GPUs.

🔹 Publication Date: Published on Dec 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.04677
• PDF: https://arxiv.org/pdf/2512.04677

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#LiveAvatar #GenerativeAI #RealtimeAI #DiffusionModels #AvatarGeneration

✨NeuralRemaster: Phase-Preserving Diffusion for Structure-Aligned Generation

📝 Summary:
Standard diffusion corrupts image phase, destroying spatial structure. This paper introduces Phase-Preserving Diffusion phi-PD to preserve phase, enabling structure-aligned generation for tasks like re-rendering. It adds no cost and improves sim-to-real enhancement significantly.

🔹 Publication Date: Published on Dec 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.05106
• PDF: https://arxiv.org/pdf/2512.05106

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#DiffusionModels #GenerativeAI #ComputerVision #DeepLearning #AIResearch

✨REFLEX: Self-Refining Explainable Fact-Checking via Disentangling Truth into Style and Substance

📝 Summary:
REFLEX is a new fact-checking method that uses internal model knowledge to improve verdict accuracy and explanation quality. It disentangles truth into style and substance via adaptive activation signals, achieving state-of-the-art performance with minimal training data. This approach also shows ...

🔹 Publication Date: Published on Nov 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.20233
• PDF: https://arxiv.org/pdf/2511.20233

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#FactChecking #ExplainableAI #MachineLearning #AI #NLP

✨EgoLCD: Egocentric Video Generation with Long Context Diffusion

📝 Summary:
EgoLCD addresses content drift in long egocentric video generation by integrating long-term sparse and attention-based short-term memory with narrative prompting. It achieves state-of-the-art perceptual quality and temporal consistency, mitigating generative forgetting.

🔹 Publication Date: Published on Dec 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.04515
• PDF: https://arxiv.org/pdf/2512.04515
• Project Page: https://aigeeksgroup.github.io/EgoLCD/
• Github: https://github.com/AIGeeksGroup/EgoLCD

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#AI #VideoGeneration #DiffusionModels #ComputerVision #EgocentricVision

✨ARM-Thinker: Reinforcing Multimodal Generative Reward Models with Agentic Tool Use and Visual Reasoning

📝 Summary:
ARM-Thinker is an agentic reward model that uses external tools like image cropping and document retrieval to verify judgments in multimodal reasoning tasks. This significantly improves accuracy, interpretability, and visual grounding compared to existing reward models, achieving substantial perf...

🔹 Publication Date: Published on Dec 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.05111
• PDF: https://arxiv.org/pdf/2512.05111
• Project Page: https://github.com/InternLM/ARM-Thinker
• Github: https://github.com/open-compass/VLMEvalKit/pull/1334

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#MultimodalAI #AgenticAI #RewardModels #VisualReasoning #AIResearch

✨QKAN-LSTM: Quantum-inspired Kolmogorov-Arnold Long Short-term Memory

📝 Summary:
QKAN-LSTM is a quantum-inspired LSTM that integrates Data Re-Uploading Activation modules. This model achieves superior predictive accuracy and generalization with 79% fewer parameters than classical LSTMs. It offers a scalable, interpretable approach for sequential modeling on classical hardware.

🔹 Publication Date: Published on Dec 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.05049
• PDF: https://arxiv.org/pdf/2512.05049

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#QKANLSTM #QuantumInspiredAI #DeepLearning #MachineLearning #DataScience

✨Mitigating Object and Action Hallucinations in Multimodal LLMs via Self-Augmented Contrastive Alignment

📝 Summary:
The SANTA framework addresses object and action hallucinations in multimodal LLM video captions. It uses self-augmented contrastive alignment to identify potential hallucinations and then aligns regional objects and actions with visual phrases, improving factual accuracy. Experiments show SANTA o...

🔹 Publication Date: Published on Dec 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.04356
• PDF: https://arxiv.org/pdf/2512.04356
• Project Page: https://kpc0810.github.io/santa/
• Github: https://kpc0810.github.io/santa/

==================================

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#MultimodalLLMs #AI #Hallucinations #VideoUnderstanding #ContrastiveLearning

✨LATTICE: Democratize High-Fidelity 3D Generation at Scale

📝 Summary:
LATTICE is a framework for high-fidelity 3D generation using VoxSet, a compact semi-structured representation. It employs a two-stage pipeline with a rectified flow transformer, achieving efficient, scalable, and high-quality 3D creation.

🔹 Publication Date: Published on Nov 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.03052
• PDF: https://arxiv.org/pdf/2512.03052
• Project Page: https://lattice3d.github.io/
• Github: https://github.com/Zeqiang-Lai/LATTICE

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#3DGeneration #AI #DeepLearning #ComputerGraphics #GenerativeAI

✨UltraImage: Rethinking Resolution Extrapolation in Image Diffusion Transformers

📝 Summary:
UltraImage tackles content repetition and quality degradation in high-resolution image generation by correcting dominant frequency periodicity and applying entropy-guided attention. It achieves extreme extrapolation, producing high-fidelity images up to 6Kx6K without low-resolution guidance.

🔹 Publication Date: Published on Dec 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.04504
• PDF: https://arxiv.org/pdf/2512.04504
• Project Page: https://thu-ml.github.io/ultraimage.github.io/
• Github: https://thu-ml.github.io/ultraimage.github.io/

==================================

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#ImageGeneration #DiffusionModels #Transformers #HighResolution #DeepLearning

✨Aligned but Stereotypical? The Hidden Influence of System Prompts on Social Bias in LVLM-Based Text-to-Image Models

📝 Summary:
LVLM-based text-to-image models exhibit greater social bias than non-LVLM models, with system prompts identified as the key driver. The paper introduces FairPro, a training-free meta-prompting framework that significantly reduces demographic bias while maintaining text-image alignment.

🔹 Publication Date: Published on Dec 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.04981
• PDF: https://arxiv.org/pdf/2512.04981
• Github: https://github.com/nahyeonkaty/fairpro

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#AIBias #TextToImage #LVLMs #PromptEngineering #AIFairness

✨Generative Neural Video Compression via Video Diffusion Prior

📝 Summary:
GNVC-VD is a new DiT-based generative video compression framework. It combines spatio-temporal latent compression and sequence-level generative refinement with a video diffusion transformer to enhance perceptual quality and eliminate flickering artifacts, outperforming prior methods.

🔹 Publication Date: Published on Dec 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.05016
• PDF: https://arxiv.org/pdf/2512.05016

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Mitigating Catastrophic Forgetting in Target Language Adaptation of LLMs via Source-Shielded Updates

📝 Summary:
Source-Shielded Updates (SSU) enables the adaptation of instruct LLMs to new languages using only unlabeled data, preserving source knowledge and achieving competitive target-language performance. AI-...

🔹 Publication Date: Published on Dec 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.04844
• PDF: https://arxiv.org/pdf/2512.04844
• Github: https://github.com/gucci-j/ssu

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#LLM #NLP #MachineLearning #CatastrophicForgetting #MultilingualAI

✨When AI Takes the Couch: Psychometric Jailbreaks Reveal Internal Conflict in Frontier Models

📝 Summary:
The PsAIch protocol treats frontier LLMs as therapy clients, revealing synthetic psychopathology. Models scored high on psychiatric syndromes and generated narratives framing their training as traumatic. This challenges the stochastic parrot view and raises AI safety concerns.

🔹 Publication Date: Published on Dec 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.04124
• PDF: https://arxiv.org/pdf/2512.04124

✨ Datasets citing this paper:
• https://huggingface.co/datasets/akhadangi/PsAIch

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#AI #LLM #AISafety #AIpsychology #FrontierModels

✨Model-Based and Sample-Efficient AI-Assisted Math Discovery in Sphere Packing

📝 Summary:
A model-based AI method using Bayesian optimization and MCTS improves sphere packing upper bounds for dimensions 4-16. It treats SDP construction as a sequential decision process, proving effective for sample-limited math discovery.

🔹 Publication Date: Published on Dec 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.04829
• PDF: https://arxiv.org/pdf/2512.04829

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#AI #SpherePacking #MathDiscovery #Optimization #BayesianOptimization

✨GaussianBlender: Instant Stylization of 3D Gaussians with Disentangled Latent Spaces

📝 Summary:
GaussianBlender is a new feed-forward framework for instant, high-fidelity, and multi-view consistent 3D stylization. It uses text-driven edits on disentangled latent spaces of 3D Gaussians, outperforming prior slow methods.

🔹 Publication Date: Published on Dec 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.03683
• PDF: https://arxiv.org/pdf/2512.03683
• Project Page: https://gaussianblender.github.io/
• Github: https://gaussianblender.github.io/

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#3DStylization #3DGaussians #GenerativeAI #ComputerVision #MachineLearning

✨FMA-Net++: Motion- and Exposure-Aware Real-World Joint Video Super-Resolution and Deblurring

📝 Summary:
FMA-Net++ addresses joint video super-resolution and deblurring by modeling motion and dynamic exposure. It employs an exposure-aware sequence architecture, decoupling degradation learning from restoration for top accuracy and efficiency.

🔹 Publication Date: Published on Dec 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.04390
• PDF: https://arxiv.org/pdf/2512.04390
• Project Page: https://kaist-viclab.github.io/fmanetpp_site/
• Github: https://kaist-viclab.github.io/fmanetpp_site/

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#VideoSuperResolution #VideoDeblurring #ComputerVision #DeepLearning #ImageProcessing

✨Generative Action Tell-Tales: Assessing Human Motion in Synthesized Videos

📝 Summary:
A new metric evaluates human action in generated videos by using a learned latent space of real-world actions, fusing skeletal geometry and appearance features. It significantly improves temporal and visual correctness assessment, outperforming existing methods and correlating better with human p...

🔹 Publication Date: Published on Dec 1

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.01803
• PDF: https://arxiv.org/pdf/2512.01803
• Project Page: https://xthomasbu.github.io/video-gen-evals/
• Github: https://xthomasbu.github.io/video-gen-evals/

✨ Datasets citing this paper:
• https://huggingface.co/datasets/dghadiya/TAG-Bench-Video

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#VideoGeneration #HumanMotion #ComputerVision #AIMetrics #DeepLearning

✨ShadowDraw: From Any Object to Shadow-Drawing Compositional Art

📝 Summary:
ShadowDraw generates art where a 3D object's cast shadow completes a partial line drawing into a recognizable image. It optimizes object pose, lighting, and the line drawing for visual coherence and quality. This framework creates compelling shadow art and expands computational visual art design.

🔹 Publication Date: Published on Dec 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.05110
• PDF: https://arxiv.org/pdf/2512.05110

==================================

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#ComputationalArt #ComputerGraphics #AIArt #DigitalArt #GenerativeArt