✨InternVideo-Next: Towards General Video Foundation Models without Video-Text Supervision

📝 Summary:
InternVideo-Next proposes a two-stage Encoder-Predictor-Decoder framework for general video representation learning without text supervision. It uses a conditional diffusion decoder to bridge pixel fidelity with semantics in Stage 1, then a latent world model in Stage 2 to learn world knowledge a...

🔹 Publication Date: Published on Dec 1

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.01342
• PDF: https://arxiv.org/pdf/2512.01342

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#VideoFoundationModels #VideoAI #DeepLearning #UnsupervisedLearning #DiffusionModels

✨How Much 3D Do Video Foundation Models Encode?

📝 Summary:
A new framework quantifies 3D understanding in Video Foundation Models VidFMs. VidFMs, trained only on video, show strong 3D awareness, often surpassing expert 3D models, providing insights for 3D AI.

🔹 Publication Date: Published on Dec 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.19949
• PDF: https://arxiv.org/pdf/2512.19949
• Project Page: https://vidfm-3d-probe.github.io/
• Github: https://vidfm-3d-probe.github.io

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#VideoFoundationModels #3DUnderstanding #ComputerVision #AIResearch #DeepLearning

🔥 Training Video Foundation Models with NVIDIA NeMo

💡 The paper addresses the challenges of training large scale high quality video foundation models that can generate high quality videos. Video foundation models have been used to simulate the real world and develop creative visual experiences but training them is difficult due to the complexity and size of video datasets. To overcome this the authors present a scalable open source pipeline using NVIDIA NeMo for training and inference of video foundation models. The pipeline provides accelerated video dataset curation multimodal data loading and parallelized video diffusion model training and inference. The authors also provide a comprehensive performance analysis highlighting best practices for efficient video foundation model training and inference. The pipeline is designed to address the challenges of training large scale video foundation models and provides a scalable and efficient solution for generating high quality videos. The results of the paper demonstrate the effectiveness of the pipeline in training video foundation models and provide insights into the best practices for efficient training and inference. Overall the paper contributes to the development of video foundation models by providing a scalable and efficient pipeline for training and inference.

📅 Published on Mar 17, 2025

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2503.12964
• PDF: https://arxiv.org/pdf/2503.12964

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#VideoFoundationModels #NVIDIANeMo #VideoDatasetCuration #MultimodalLearning #VideoDiffusionModels