✨3DreamBooth: High-Fidelity 3D Subject-Driven Video Generation Model

📝 Summary:
This novel framework enables 3D-aware video customization by decoupling spatial geometry from temporal motion using 1-frame optimization to build robust 3D priors. It also incorporates a visual conditioning module for enhanced texture generation and faster convergence.

🔹 Publication Date: Published on Mar 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.18524
• PDF: https://arxiv.org/pdf/2603.18524
• Project Page: https://ko-lani.github.io/3DreamBooth
• Github: https://github.com/Ko-Lani/3DreamBooth

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨MonoArt: Progressive Structural Reasoning for Monocular Articulated 3D Reconstruction

📝 Summary:
MonoArt presents a unified framework for reconstructing articulated 3D objects from single images through progressive structural reasoning that enables stable articulation inference without external t...

🔹 Publication Date: Published on Mar 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.19231
• PDF: https://arxiv.org/pdf/2603.19231
• Project Page: https://lihaitian.com/MonoArt/
• Github: https://github.com/Quest4Science/MonoArt

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✨MOSS-TTS Technical Report

📝 Summary:
MOSS-TTS is a speech generation model using discrete audio tokens and autoregressive modeling with capabilities for voice cloning, pronunciation control, and long-form generation across multiple langu...

🔹 Publication Date: Published on Mar 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.18090
• PDF: https://arxiv.org/pdf/2603.18090
• Project Page: https://mosi.cn/models/moss-tts
• Github: https://github.com/OpenMOSS/MOSS-TTS

🔹 Models citing this paper:
• https://huggingface.co/OpenMOSS-Team/MOSS-TTS
• https://huggingface.co/OpenMOSS-Team/MOSS-TTS-Realtime
• https://huggingface.co/OpenMOSS-Team/MOSS-TTS-Local-Transformer

✨ Spaces citing this paper:
• https://huggingface.co/spaces/OpenMOSS-Team/MOSS-TTS
• https://huggingface.co/spaces/Pendrokar/TTS-Spaces-Arena
• https://huggingface.co/spaces/JymNils/MOSS-TTS

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✨ReactMotion: Generating Reactive Listener Motions from Speaker Utterance

📝 Summary:
This paper introduces ReactMotion, a framework for generating natural listener body motions that react appropriately to speaker utterances. It uses a large dataset and preference-based training to create diverse, realistic responses, outperforming prior methods.

🔹 Publication Date: Published on Mar 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.15083
• PDF: https://arxiv.org/pdf/2603.15083

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#AI #MachineLearning #HumanComputerInteraction #GenerativeAI #ComputerAnimation

✨VID-AD: A Dataset for Image-Level Logical Anomaly Detection under Vision-Induced Distraction

📝 Summary:
VID-AD is a dataset for logical anomaly detection in industrial inspection, specifically addressing challenges from visual distractions. A new language-based framework is also proposed, which uses text descriptions and contrastive learning to capture logical attributes.

🔹 Publication Date: Published on Mar 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.13964
• PDF: https://arxiv.org/pdf/2603.13964
• Github: https://github.com/nkthiroto/VID-AD

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

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#AnomalyDetection #IndustrialInspection #ComputerVision #MachineLearning #Datasets

✨Beyond Single Tokens: Distilling Discrete Diffusion Models via Discrete MMD

📝 Summary:
Discrete Moment Matching Distillation (D-MMD) enables effective distillation of discrete diffusion models by adapting continuous-domain techniques, achieving superior performance compared to previous ...

🔹 Publication Date: Published on Mar 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.20155
• PDF: https://arxiv.org/pdf/2603.20155

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

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✨How Well Does Generative Recommendation Generalize?

📝 Summary:
Generative recommendation models excel at generalization tasks while item ID-based models perform better at memorization, with a complementary approach showing improved recommendation performance thro...

🔹 Publication Date: Published on Mar 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.19809
• PDF: https://arxiv.org/pdf/2603.19809
• Github: https://github.com/Jamesding000/MemGen-GR

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

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✨Teaching an Agent to Sketch One Part at a Time

📝 Summary:
Researchers developed an agent that generates vector sketches incrementally, one part at a time. It uses a multi-modal language model and process-reward reinforcement learning with a new part-annotated dataset. This enables controllable and editable text-to-vector sketch generation.

🔹 Publication Date: Published on Mar 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.19500
• PDF: https://arxiv.org/pdf/2603.19500

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

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#AI #GenerativeAI #MachineLearning #ComputerVision #ReinforcementLearning

✨AgentDS Technical Report: Benchmarking the Future of Human-AI Collaboration in Domain-Specific Data Science

📝 Summary:
AgentDS benchmark evaluates AI agents and human-AI collaboration in domain-specific data science tasks, revealing continued necessity of human expertise despite advances in large language models and A...

🔹 Publication Date: Published on Mar 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.19005
• PDF: https://arxiv.org/pdf/2603.19005
• Project Page: https://agentds.org/

✨ Datasets citing this paper:
• https://huggingface.co/datasets/lainmn/AgentDS-Insurance
• https://huggingface.co/datasets/lainmn/AgentDS-RetailBanking
• https://huggingface.co/datasets/lainmn/AgentDS-Manufacturing

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

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✨EgoForge: Goal-Directed Egocentric World Simulator

📝 Summary:
EgoForge is an egocentric goal-directed world simulator that generates coherent first-person video rollouts from minimal static inputs using trajectory-level reward-guided refinement during diffusion ...

🔹 Publication Date: Published on Mar 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.20169
• PDF: https://arxiv.org/pdf/2603.20169
• Project Page: https://plan-lab.github.io/projects/egoforge

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

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