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All my resources will be free and unrestricted there. My goal is to build a clean community exclusively for smart programmers, and I believe Signal is the most suitable platform for this (Signal is the second most popular app after WhatsApp in the US), making it particularly suitable for us as programmers.
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✨SCAIL: Towards Studio-Grade Character Animation via In-Context Learning of 3D-Consistent Pose Representations
📝 Summary:
SCAIL is a framework that improves character animation to studio-grade quality. It uses a novel 3D pose representation and a diffusion-transformer with full-context pose injection, achieving state-of-the-art realism and reliability.
🔹 Publication Date: Published on Dec 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.05905
• PDF: https://arxiv.org/pdf/2512.05905
🔹 Models citing this paper:
• https://huggingface.co/zai-org/SCAIL-Preview
==================================
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#CharacterAnimation #AI #3DAnimation #DeepLearning #ComputerGraphics
📝 Summary:
SCAIL is a framework that improves character animation to studio-grade quality. It uses a novel 3D pose representation and a diffusion-transformer with full-context pose injection, achieving state-of-the-art realism and reliability.
🔹 Publication Date: Published on Dec 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.05905
• PDF: https://arxiv.org/pdf/2512.05905
🔹 Models citing this paper:
• https://huggingface.co/zai-org/SCAIL-Preview
==================================
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#CharacterAnimation #AI #3DAnimation #DeepLearning #ComputerGraphics
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✨TimesNet-Gen: Deep Learning-based Site Specific Strong Motion Generation
📝 Summary:
TimesNet-Gen is a time-domain deep learning model that effectively synthesizes site-specific strong ground motion records. It uses a station-specific latent bottleneck and outperforms a spectrogram-based baseline, improving earthquake risk assessment.
🔹 Publication Date: Published on Dec 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.04694
• PDF: https://arxiv.org/pdf/2512.04694
• Project Page: https://huggingface.co/spaces/Barisylmz/TimesNet-Gen
• Github: https://github.com/brsylmz23/TimesNet-Gen/tree/main
✨ Spaces citing this paper:
• https://huggingface.co/spaces/Barisylmz/TimesNet-Gen
==================================
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#DeepLearning #EarthquakeEngineering #Seismology #GroundMotion #AI
📝 Summary:
TimesNet-Gen is a time-domain deep learning model that effectively synthesizes site-specific strong ground motion records. It uses a station-specific latent bottleneck and outperforms a spectrogram-based baseline, improving earthquake risk assessment.
🔹 Publication Date: Published on Dec 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.04694
• PDF: https://arxiv.org/pdf/2512.04694
• Project Page: https://huggingface.co/spaces/Barisylmz/TimesNet-Gen
• Github: https://github.com/brsylmz23/TimesNet-Gen/tree/main
✨ Spaces citing this paper:
• https://huggingface.co/spaces/Barisylmz/TimesNet-Gen
==================================
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#DeepLearning #EarthquakeEngineering #Seismology #GroundMotion #AI
✨InkSight: Offline-to-Online Handwriting Conversion by Learning to Read and Write
📝 Summary:
InkSight converts offline handwriting to digital ink using novel reading and writing priors. This approach effectively derenders text from diverse photos, generalizing beyond its training and requiring less paired data than prior methods.
🔹 Publication Date: Published on Feb 8, 2024
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2402.05804
• PDF: https://arxiv.org/pdf/2402.05804
• Project Page: https://charlieleee.github.io/publication/inksight
• Github: https://github.com/google-research/inksight
🔹 Models citing this paper:
• https://huggingface.co/Derendering/InkSight-Small-p
✨ Datasets citing this paper:
• https://huggingface.co/datasets/Derendering/InkSight-Derenderings
✨ Spaces citing this paper:
• https://huggingface.co/spaces/Derendering/Model-Output-Playground
==================================
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#HandwritingConversion #ComputerVision #DeepLearning #AIResearch #DocumentDigitization
📝 Summary:
InkSight converts offline handwriting to digital ink using novel reading and writing priors. This approach effectively derenders text from diverse photos, generalizing beyond its training and requiring less paired data than prior methods.
🔹 Publication Date: Published on Feb 8, 2024
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2402.05804
• PDF: https://arxiv.org/pdf/2402.05804
• Project Page: https://charlieleee.github.io/publication/inksight
• Github: https://github.com/google-research/inksight
🔹 Models citing this paper:
• https://huggingface.co/Derendering/InkSight-Small-p
✨ Datasets citing this paper:
• https://huggingface.co/datasets/Derendering/InkSight-Derenderings
✨ Spaces citing this paper:
• https://huggingface.co/spaces/Derendering/Model-Output-Playground
==================================
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#HandwritingConversion #ComputerVision #DeepLearning #AIResearch #DocumentDigitization
arXiv.org
InkSight: Offline-to-Online Handwriting Conversion by Teaching...
Digital note-taking is gaining popularity, offering a durable, editable, and easily indexable way of storing notes in a vectorized form, known as digital ink. However, a substantial gap remains...
✨SQ-format: A Unified Sparse-Quantized Hardware-friendly Data Format for LLMs
📝 Summary:
The SQ-format is a unified sparse-quantized data format for LLM post-training quantization. It improves accuracy and efficiency balance by combining sparse and low-precision matrix multiplications. This enables better performance and throughput, especially for outlier activations, supporting next...
🔹 Publication Date: Published on Dec 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.05409
• PDF: https://arxiv.org/pdf/2512.05409
==================================
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✓ https://news.1rj.ru/str/DataScienceT
#LLMs #Quantization #SparseML #HardwareAcceleration #AIResearch
📝 Summary:
The SQ-format is a unified sparse-quantized data format for LLM post-training quantization. It improves accuracy and efficiency balance by combining sparse and low-precision matrix multiplications. This enables better performance and throughput, especially for outlier activations, supporting next...
🔹 Publication Date: Published on Dec 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.05409
• PDF: https://arxiv.org/pdf/2512.05409
==================================
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#LLMs #Quantization #SparseML #HardwareAcceleration #AIResearch
❤1
✨M3DR: Towards Universal Multilingual Multimodal Document Retrieval
📝 Summary:
M3DR is a framework for multilingual multimodal document retrieval that uses contrastive training to achieve robust cross-lingual and cross-modal alignment. It overcomes English-centric limitations, showing state-of-the-art performance across 22 diverse languages.
🔹 Publication Date: Published on Dec 3
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.03514
• PDF: https://arxiv.org/pdf/2512.03514
• Project Page: https://www.cognitivelab.in/blog/introducing-netraembed
• Github: https://github.com/adithya-s-k/colpali
🔹 Models citing this paper:
• https://huggingface.co/Cognitive-Lab/NetraEmbed
• https://huggingface.co/Cognitive-Lab/ColNetraEmbed
✨ Datasets citing this paper:
• https://huggingface.co/datasets/Cognitive-Lab/NayanaIR-CrossBench
✨ Spaces citing this paper:
• https://huggingface.co/spaces/AdithyaSK/NetraEmbed
==================================
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✓ https://news.1rj.ru/str/DataScienceT
#MultimodalAI #InformationRetrieval #NLP #CrossLingualAI #MachineLearning
📝 Summary:
M3DR is a framework for multilingual multimodal document retrieval that uses contrastive training to achieve robust cross-lingual and cross-modal alignment. It overcomes English-centric limitations, showing state-of-the-art performance across 22 diverse languages.
🔹 Publication Date: Published on Dec 3
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.03514
• PDF: https://arxiv.org/pdf/2512.03514
• Project Page: https://www.cognitivelab.in/blog/introducing-netraembed
• Github: https://github.com/adithya-s-k/colpali
🔹 Models citing this paper:
• https://huggingface.co/Cognitive-Lab/NetraEmbed
• https://huggingface.co/Cognitive-Lab/ColNetraEmbed
✨ Datasets citing this paper:
• https://huggingface.co/datasets/Cognitive-Lab/NayanaIR-CrossBench
✨ Spaces citing this paper:
• https://huggingface.co/spaces/AdithyaSK/NetraEmbed
==================================
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#MultimodalAI #InformationRetrieval #NLP #CrossLingualAI #MachineLearning
arXiv.org
M3DR: Towards Universal Multilingual Multimodal Document Retrieval
Multimodal document retrieval systems have shown strong progress in aligning visual and textual content for semantic search. However, most existing approaches remain heavily English-centric,...
❤1
✨EMMA: Efficient Multimodal Understanding, Generation, and Editing with a Unified Architecture
📝 Summary:
EMMA is an efficient unified architecture for multimodal tasks like understanding, generation, and editing. It uses novel components including an autoencoder, channel-wise concatenation, and mixture-of-experts. EMMA achieves superior performance and efficiency over state-of-the-art unified models.
🔹 Publication Date: Published on Dec 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.04810
• PDF: https://arxiv.org/pdf/2512.04810
• Project Page: https://emma-umm.github.io/emma/
• Github: https://emma-umm.github.io/emma/
==================================
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#MultimodalAI #GenerativeAI #DeepLearning #AIArchitecture #EfficientAI
📝 Summary:
EMMA is an efficient unified architecture for multimodal tasks like understanding, generation, and editing. It uses novel components including an autoencoder, channel-wise concatenation, and mixture-of-experts. EMMA achieves superior performance and efficiency over state-of-the-art unified models.
🔹 Publication Date: Published on Dec 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.04810
• PDF: https://arxiv.org/pdf/2512.04810
• Project Page: https://emma-umm.github.io/emma/
• Github: https://emma-umm.github.io/emma/
==================================
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#MultimodalAI #GenerativeAI #DeepLearning #AIArchitecture #EfficientAI
❤3
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✨From FLOPs to Footprints: The Resource Cost of Artificial Intelligence
📝 Summary:
This study quantifies the material footprint of AI training, analyzing Nvidia A100 GPUs heavy metal composition. Training GPT-4 demands thousands of GPUs, leading to tons of toxic waste. Optimizing hardware use and lifespan can significantly cut these material costs.
🔹 Publication Date: Published on Dec 3
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.04142
• PDF: https://arxiv.org/pdf/2512.04142
✨ Spaces citing this paper:
• https://huggingface.co/spaces/sophia-falk/flops-2-footprints
==================================
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#AIFootprint #AISustainability #GreenAI #ElectronicWaste #TechEthics
📝 Summary:
This study quantifies the material footprint of AI training, analyzing Nvidia A100 GPUs heavy metal composition. Training GPT-4 demands thousands of GPUs, leading to tons of toxic waste. Optimizing hardware use and lifespan can significantly cut these material costs.
🔹 Publication Date: Published on Dec 3
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.04142
• PDF: https://arxiv.org/pdf/2512.04142
✨ Spaces citing this paper:
• https://huggingface.co/spaces/sophia-falk/flops-2-footprints
==================================
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✓ https://news.1rj.ru/str/DataScienceT
#AIFootprint #AISustainability #GreenAI #ElectronicWaste #TechEthics
❤1
✨Active Video Perception: Iterative Evidence Seeking for Agentic Long Video Understanding
📝 Summary:
Active Video Perception AVP improves long video understanding by actively seeking query-relevant evidence. It uses an iterative plan-observe-reflect process, acquiring compact evidence directly from pixels. This achieves higher accuracy with reduced computational cost.
🔹 Publication Date: Published on Dec 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.05774
• PDF: https://arxiv.org/pdf/2512.05774
==================================
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✓ https://news.1rj.ru/str/DataScienceT
#VideoUnderstanding #ActiveLearning #ComputerVision #AIResearch #DeepLearning
📝 Summary:
Active Video Perception AVP improves long video understanding by actively seeking query-relevant evidence. It uses an iterative plan-observe-reflect process, acquiring compact evidence directly from pixels. This achieves higher accuracy with reduced computational cost.
🔹 Publication Date: Published on Dec 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.05774
• PDF: https://arxiv.org/pdf/2512.05774
==================================
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#VideoUnderstanding #ActiveLearning #ComputerVision #AIResearch #DeepLearning
✨Taxonomy-Adaptive Moderation Model with Robust Guardrails for Large Language Models
📝 Summary:
Roblox Guard 1.0 is an instruction fine-tuned LLM that enhances safety through comprehensive input-output moderation. It uses a pipeline of LLMs, generalizes to new safety taxonomies, and performs strongly on out-of-domain benchmarks. A new evaluation benchmark, RobloxGuard-Eval, is also released.
🔹 Publication Date: Published on Dec 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.05339
• PDF: https://arxiv.org/pdf/2512.05339
==================================
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✓ https://news.1rj.ru/str/DataScienceT
#LLM #AISafety #AI #MachineLearning #NLP
📝 Summary:
Roblox Guard 1.0 is an instruction fine-tuned LLM that enhances safety through comprehensive input-output moderation. It uses a pipeline of LLMs, generalizes to new safety taxonomies, and performs strongly on out-of-domain benchmarks. A new evaluation benchmark, RobloxGuard-Eval, is also released.
🔹 Publication Date: Published on Dec 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.05339
• PDF: https://arxiv.org/pdf/2512.05339
==================================
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#LLM #AISafety #AI #MachineLearning #NLP
❤1
✨From Segments to Scenes: Temporal Understanding in Autonomous Driving via Vision-Language Model
📝 Summary:
The TAD benchmark is introduced to evaluate temporal understanding in autonomous driving, addressing a gap where current VLMs perform poorly. It reveals that state-of-the-art models show substandard accuracy in this domain. Two training-free solutions, Scene-CoT and TCogMap, are proposed, improvi...
🔹 Publication Date: Published on Dec 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.05277
• PDF: https://arxiv.org/pdf/2512.05277
• Github: https://github.com/vbdi/tad_bench
==================================
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#AutonomousDriving #VisionLanguageModels #ComputerVision #AIResearch #DeepLearning
📝 Summary:
The TAD benchmark is introduced to evaluate temporal understanding in autonomous driving, addressing a gap where current VLMs perform poorly. It reveals that state-of-the-art models show substandard accuracy in this domain. Two training-free solutions, Scene-CoT and TCogMap, are proposed, improvi...
🔹 Publication Date: Published on Dec 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.05277
• PDF: https://arxiv.org/pdf/2512.05277
• Github: https://github.com/vbdi/tad_bench
==================================
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#AutonomousDriving #VisionLanguageModels #ComputerVision #AIResearch #DeepLearning
❤3
🤖🧠 Distil-Whisper: Faster, Smaller, and Smarter Speech Recognition by Hugging Face
🗓️ 08 Dec 2025
📚 AI News & Trends
The evolution of Automatic Speech Recognition (ASR) has reshaped how humans interact with technology. From dictation tools and live trannoscription to smart assistants and media captioning, ASR technology continues to bridge the gap between speech and digital communication. However, achieving real-time, high-accuracy trannoscription often comes at the cost of heavy computational requirements until now. Enter ...
#DistilWhisper #FasterSpeechRecognition #SmallerModels #HuggingFace #ASRTechnology #RealTimeTrannoscription
🗓️ 08 Dec 2025
📚 AI News & Trends
The evolution of Automatic Speech Recognition (ASR) has reshaped how humans interact with technology. From dictation tools and live trannoscription to smart assistants and media captioning, ASR technology continues to bridge the gap between speech and digital communication. However, achieving real-time, high-accuracy trannoscription often comes at the cost of heavy computational requirements until now. Enter ...
#DistilWhisper #FasterSpeechRecognition #SmallerModels #HuggingFace #ASRTechnology #RealTimeTrannoscription
✨Colon-X: Advancing Intelligent Colonoscopy from Multimodal Understanding to Clinical Reasoning
📝 Summary:
Colon-X introduces ColonR1, a novel reasoning-centric model for intelligent colonoscopy. It achieves 56.61% accuracy, outperforming traditional methods by 25.22% under data scarcity, by leveraging new comprehensive multimodal datasets.
🔹 Publication Date: Published on Dec 3
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.03667
• PDF: https://arxiv.org/pdf/2512.03667
• Github: https://github.com/ai4colonoscopy/Colon-X
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Colon-X introduces ColonR1, a novel reasoning-centric model for intelligent colonoscopy. It achieves 56.61% accuracy, outperforming traditional methods by 25.22% under data scarcity, by leveraging new comprehensive multimodal datasets.
🔹 Publication Date: Published on Dec 3
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.03667
• PDF: https://arxiv.org/pdf/2512.03667
• Github: https://github.com/ai4colonoscopy/Colon-X
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
✨DoVer: Intervention-Driven Auto Debugging for LLM Multi-Agent Systems
📝 Summary:
DoVer is an intervention-driven debugging approach for LLM multi-agent systems. It validates failure hypotheses and measures progress via targeted interventions, improving reliability. DoVer converts 18-49% of failed tasks into successes, offering an outcome-oriented debugging method.
🔹 Publication Date: Published on Dec 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.06749
• PDF: https://arxiv.org/pdf/2512.06749
• Project Page: https://aka.ms/DoVer
==================================
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#LLM #MultiAgentSystems #Debugging #AI #Research
📝 Summary:
DoVer is an intervention-driven debugging approach for LLM multi-agent systems. It validates failure hypotheses and measures progress via targeted interventions, improving reliability. DoVer converts 18-49% of failed tasks into successes, offering an outcome-oriented debugging method.
🔹 Publication Date: Published on Dec 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.06749
• PDF: https://arxiv.org/pdf/2512.06749
• Project Page: https://aka.ms/DoVer
==================================
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#LLM #MultiAgentSystems #Debugging #AI #Research
✨Beyond Real: Imaginary Extension of Rotary Position Embeddings for Long-Context LLMs
📝 Summary:
The paper proposes a method to enhance Rotary Position Embeddings by utilizing both the real and imaginary components of the complex-valued dot product, improving long-context modeling in Large Langua...
🔹 Publication Date: Published on Dec 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.07525
• PDF: https://arxiv.org/pdf/2512.07525
• Github: https://github.com/OpenMOSS/rope_pp
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
The paper proposes a method to enhance Rotary Position Embeddings by utilizing both the real and imaginary components of the complex-valued dot product, improving long-context modeling in Large Langua...
🔹 Publication Date: Published on Dec 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.07525
• PDF: https://arxiv.org/pdf/2512.07525
• Github: https://github.com/OpenMOSS/rope_pp
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
✨LongCat-Image Technical Report
📝 Summary:
LongCat-Image is a bilingual open-source foundation model for image generation that addresses multilingual text rendering, photorealism, and deployment efficiency through rigorous data curation, compa...
🔹 Publication Date: Published on Dec 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.07584
• PDF: https://arxiv.org/pdf/2512.07584
• Project Page: https://longcat.chat/
• Github: https://github.com/meituan-longcat/LongCat-Image
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📝 Summary:
LongCat-Image is a bilingual open-source foundation model for image generation that addresses multilingual text rendering, photorealism, and deployment efficiency through rigorous data curation, compa...
🔹 Publication Date: Published on Dec 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.07584
• PDF: https://arxiv.org/pdf/2512.07584
• Project Page: https://longcat.chat/
• Github: https://github.com/meituan-longcat/LongCat-Image
==================================
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✓ https://news.1rj.ru/str/DataScienceT
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✨EgoEdit: Dataset, Real-Time Streaming Model, and Benchmark for Egocentric Video Editing
📝 Summary:
EgoEdit is a real-time, instruction-following egocentric video editor that addresses challenges in handling egomotion and hand-object interactions, outperforming existing methods on egocentric editing...
🔹 Publication Date: Published on Dec 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.06065
• PDF: https://arxiv.org/pdf/2512.06065
• Project Page: https://snap-research.github.io/EgoEdit/
• Github: https://github.com/snap-research/EgoEdit
==================================
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📝 Summary:
EgoEdit is a real-time, instruction-following egocentric video editor that addresses challenges in handling egomotion and hand-object interactions, outperforming existing methods on egocentric editing...
🔹 Publication Date: Published on Dec 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.06065
• PDF: https://arxiv.org/pdf/2512.06065
• Project Page: https://snap-research.github.io/EgoEdit/
• Github: https://github.com/snap-research/EgoEdit
==================================
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✨Scaling Zero-Shot Reference-to-Video Generation
📝 Summary:
Saber is a scalable zero-shot framework for reference-to-video generation that uses video-text pairs to learn identity-consistent representations and outperforms models trained with explicit reference...
🔹 Publication Date: Published on Dec 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.06905
• PDF: https://arxiv.org/pdf/2512.06905
• Project Page: https://franciszzj.github.io/Saber/
==================================
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📝 Summary:
Saber is a scalable zero-shot framework for reference-to-video generation that uses video-text pairs to learn identity-consistent representations and outperforms models trained with explicit reference...
🔹 Publication Date: Published on Dec 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.06905
• PDF: https://arxiv.org/pdf/2512.06905
• Project Page: https://franciszzj.github.io/Saber/
==================================
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✓ https://news.1rj.ru/str/DataScienceT
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