✨OpenVoice: Versatile Instant Voice Cloning

📝 Summary:
OpenVoice is a versatile voice cloning method using a short audio clip. It provides flexible control over voice styles and achieves zero-shot cross-lingual cloning for new languages without extensive training data. It is also highly efficient.

🔹 Publication Date: Published on Dec 3, 2023

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2312.01479
• PDF: https://arxiv.org/pdf/2312.01479
• Github: https://github.com/myshell-ai/openvoice

🔹 Models citing this paper:
• https://huggingface.co/rsxdalv/OpenVoiceV2
• https://huggingface.co/ameerazam08/Udiff
• https://huggingface.co/flopml/OpenVoice-v2

✨ Datasets citing this paper:
• https://huggingface.co/datasets/tsinghua-ee/QualiSpeech
• https://huggingface.co/datasets/dlxjj/Openvoice
• https://huggingface.co/datasets/Pendrokar/open_tts_tracker

✨ Spaces citing this paper:
• https://huggingface.co/spaces/Russell1123213123/testOpenVoice
• https://huggingface.co/spaces/gauthamk28/gauthamk28_voice
• https://huggingface.co/spaces/blayks07/OpenVoice-main

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#VoiceCloning #AIResearch #SpeechSynthesis #ZeroShotLearning #CrossLingualAI

✨Dynamic Reflections: Probing Video Representations with Text Alignment

📝 Summary:
This work presents the first comprehensive study on video-text representation alignment. It reveals alignment depends on data richness and correlates with downstream task performance, suggesting its value for general video understanding. This introduces video-text alignment as a zero-shot method ...

🔹 Publication Date: Published on Nov 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.02767
• PDF: https://arxiv.org/pdf/2511.02767
• Github: https://video-prh.github.io/

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#VideoUnderstanding #TextAlignment #VideoTextAI #ZeroShotLearning #RepresentationLearning

✨NAF: Zero-Shot Feature Upsampling via Neighborhood Attention Filtering

📝 Summary:
NAF upsamples Vision Foundation Model features zero-shot by learning adaptive spatial-and-content weights. It outperforms VFM-specific upsamplers without retraining, achieving state-of-the-art performance across various tasks efficiently.

🔹 Publication Date: Published on Nov 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.18452
• PDF: https://arxiv.org/pdf/2511.18452
• Github: https://github.com/valeoai/NAF?tab=readme-ov-file

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#ZeroShotLearning #ComputerVision #FeatureUpsampling #DeepLearning #AIResearch

✨NAF: Zero-Shot Feature Upsampling via Neighborhood Attention Filtering

📝 Summary:
NAF upsamples Vision Foundation Model features zero-shot by learning adaptive spatial-and-content weights. It outperforms VFM-specific upsamplers without retraining, achieving state-of-the-art performance across various tasks efficiently.

🔹 Publication Date: Published on Nov 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.18452
• PDF: https://arxiv.org/pdf/2511.18452
• Github: https://github.com/valeoai/NAF?tab=readme-ov-file

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#ZeroShotLearning #ComputerVision #FeatureUpsampling #DeepLearning #AIResearch

✨MG-Nav: Dual-Scale Visual Navigation via Sparse Spatial Memory

📝 Summary:
MG-Nav is a dual-scale framework for zero-shot visual navigation, unifying global memory-guided planning via a Sparse Spatial Memory Graph with local geometry-enhanced control using a VGGT-adapter. It achieves state-of-the-art performance and robustness in unseen environments.

🔹 Publication Date: Published on Nov 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.22609
• PDF: https://arxiv.org/pdf/2511.22609

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#VisualNavigation #Robotics #AI #ComputerVision #ZeroShotLearning

✨Dream2Flow: Bridging Video Generation and Open-World Manipulation with 3D Object Flow

📝 Summary:
Dream2Flow bridges video generation and robotic control using 3D object flow. It reconstructs 3D object motions from generated videos, enabling zero-shot manipulation of diverse objects through trajectory tracking without task-specific demonstrations.

🔹 Publication Date: Published on Dec 31, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24766
• PDF: https://arxiv.org/pdf/2512.24766

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#VideoGeneration #Robotics #3DVision #AI #ZeroShotLearning

✨VidVec: Unlocking Video MLLM Embeddings for Video-Text Retrieval

📝 Summary:
VidVec uses intermediate MLLM layers for zero-shot video-text retrieval. A novel text-based alignment, mapping video captions to summaries, learns embeddings without visual supervision. It achieves state-of-the-art results on video retrieval benchmarks.

🔹 Publication Date: Published on Feb 8

🔹 Paper Links:
• arXiv Page: https://www.arxiv.org/abs/2602.08099
• PDF: https://arxiv.org/pdf/2602.08099
• Project Page: https://iyttor.github.io/VidVec
• Github: https://iyttor.github.io/VidVec

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#VideoTextRetrieval #MLLM #Embeddings #ZeroShotLearning #AI

✨LaS-Comp: Zero-shot 3D Completion with Latent-Spatial Consistency

📝 Summary:
LaS-Comp is a zero-shot 3D shape completion method that leverages 3D foundation models. It uses a two-stage approach for faithful reconstruction and seamless boundary refinement. This training-free framework outperforms prior state-of-the-art methods.

🔹 Publication Date: Published on Feb 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.18735
• PDF: https://arxiv.org/pdf/2602.18735
• Github: https://github.com/DavidYan2001/LaS-Comp

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#3DCompletion #ZeroShotLearning #FoundationModels #ComputerVision #AI

✨Fast-FoundationStereo: Real-Time Zero-Shot Stereo Matching

📝 Summary:
Fast-FoundationStereo achieves real-time zero-shot stereo matching, bridging the gap between slow robust models and fast specialized ones. It employs distillation, architecture search, and pruning, running over 10x faster with similar accuracy to prior foundation models. This sets a new state-of-...

🔹 Publication Date: Published on Dec 11, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.11130
• PDF: https://arxiv.org/pdf/2512.11130
• Project Page: https://nvlabs.github.io/Fast-FoundationStereo/
• Github: https://github.com/NVlabs/Fast-FoundationStereo

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#StereoMatching #ComputerVision #RealTimeAI #ZeroShotLearning #DeepLearning

🔥 IndexTTS: An Industrial-Level Controllable and Efficient Zero-Shot Text-To-Speech System

💡 The paper introduces IndexTTS, an enhanced text-to-speech system that combines the XTTS and Tortoise models to achieve improved naturalness, voice cloning, and controllable usage. The system addresses the limitations of existing text-to-speech systems, particularly in Chinese scenarios where polyphonic characters and long-tail characters can be challenging to pronounce. To overcome this, the authors propose a hybrid character-pinyin modeling approach that allows for more controllable pronunciations.

The authors also compare Vector Quantization with Finite-Scalar Quantization for codebook utilization of acoustic speech tokens, and introduce a conformer-based speech conditional encoder and BigVGAN2 to enhance voice cloning. The results show that IndexTTS achieves significant improvements in naturalness, content consistency, and zero-shot voice cloning compared to the XTTS model.

In comparison to other popular open-source text-to-speech systems, IndexTTS has a relatively simple training process, more controllable usage, and faster inference speed, while also surpassing their performance. The system is designed to be efficient and controllable, making it suitable for industrial-level applications. The authors provide demos of the system, which are available for evaluation. Overall, the paper presents a novel approach to text-to-speech synthesis that achieves state-of-the-art results and has the potential for practical applications.

📅 Published on Feb 8, 2025

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2502.05512
• PDF: https://arxiv.org/pdf/2502.05512
• Project Page: https://index-tts.github.io

🤖 Models citing this paper:
• https://huggingface.co/IndexTeam/IndexTTS-2
• https://huggingface.co/IndexTeam/Index-TTS
• https://huggingface.co/taraskurtizan/IndexTTS-2

📊 Datasets citing this paper:
• https://huggingface.co/datasets/echodict/index-tts

🚀 Spaces citing this paper:
• https://huggingface.co/spaces/IndexTeam/IndexTTS
• https://huggingface.co/spaces/Pendrokar/TTS-Spaces-Arena
• https://huggingface.co/spaces/alexnasa/OutofLipSync

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📢 By: https://xn--r1a.website/PaperNexus

#TextToSpeechSystems #ZeroShotLearning #VoiceCloningTechnology #ControllableSpeechSynthesis #SpeechRecognitionModels