add readme

Signed-off-by: Ethan He <yihuih@nvidia.com>

Author: Ethan He

README.md

@@ -1 +1,30 @@
-# NeMo VFM
+# NeMo VFM: video foundation model collection
+
+NeMo VFM is a state-of-the-art framework for fast, large-scale training and inference of video world models. It unifies the latest diffusion-based and autoregressive techniques, prioritizing efficiency and performance from research prototyping to production deployment.
+
+## Projects
+
+This collection consists of 4 projects:
+1. [Scalable diffusion training framework](nemo_vfm/diffusion/readme.rst)
+2. [Accelerated diffusion world models](nemo_vfm/physicalai/Cosmos/cosmos1/models/diffusion/README.md)
+3. [Accelerated autoregressive world models](nemo_vfm/physicalai/Cosmos/cosmos1/models/autoregressive/README.md)
+4. [Sparse attention for efficient diffusion inference](nemo_vfm/sparse_attention/README.md)
+
+## Citations
+
+If you find our code useful, please consider citing the following papers:
+```bibtex
+@article{patel2025training,
+  title={Training Video Foundation Models with NVIDIA NeMo},
+  author={Patel, Zeeshan and He, Ethan and Mannan, Parth and Ren, Xiaowei and Wolf, Ryan and Agarwal, Niket and Huffman, Jacob and Wang, Zhuoyao and Wang, Carl and Chang, Jack and others},
+  journal={arXiv preprint arXiv:2503.12964},
+  year={2025}
+}
+
+@article{agarwal2025cosmos,
+  title={Cosmos world foundation model platform for physical ai},
+  author={Agarwal, Niket and Ali, Arslan and Bala, Maciej and Balaji, Yogesh and Barker, Erik and Cai, Tiffany and Chattopadhyay, Prithvijit and Chen, Yongxin and Cui, Yin and Ding, Yifan and others},
+  journal={arXiv preprint arXiv:2501.03575},
+  year={2025}
+}
+```