From c16578d437e12b891dab2999919b3e1e9b9536dd Mon Sep 17 00:00:00 2001 From: Sophia Huang Date: Thu, 5 Jun 2025 17:18:37 -0700 Subject: [PATCH] Update README.md --- README.md | 27 +++++++++++++++++++-------- 1 file changed, 19 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index bf3c75a..c044366 100644 --- a/README.md +++ b/README.md @@ -1,18 +1,29 @@

- NVIDIA Cosmos Header + New GitHub page for NVIDIA Cosmos: https://github.com/nvidia-cosmos

-Thank you all for the valuable feedback! We have restructured the codebase to make it easier to use and contribute to. +# Nvidia Cosmos + +Cosmos World Foundation Models come in three model types which can all be customized in post-training: [cosmos-predict](https://github.com/nvidia-cosmos/cosmos-predict1), [cosmos-transfer](https://github.com/nvidia-cosmos/cosmos-transfer1), and [cosmos-reason](https://github.com/nvidia-cosmos/cosmos-reason1): + +| | Predict | Transfer | Reason | +| ----- | :---: | :---: | :---: | +| **Type** | World Generation | Multi-Controlnet | Reasoning VLM | +| **Function** | Predict novel future frames given initial frames | Transfer existing control frames into photoreal frames within a video clip | Reason against frames within a video clip | +| **Use Cases** | Data Generation & Policy Evaluation | Data Augmentation | Data Curation | +| **Inputs** | Text, Image, Video | Multiple Video Modalities such as RGB, Depth, Segmentation, and more. | Video & Text | +| **Outputs** | Video | Video | Text | + +# + +# Use Cases in Physical AI Development + +Our world foundation models are purpose-built to accelerate improving performance in downstream model tasks in various stages, as illustrated here in the flywheel.

- New GitHub page for NVIDIA Cosmos: https://github.com/nvidia-cosmos + NVIDIA Cosmos Data Flywheel

-NVIDIA Cosmos now includes three subprojects: - -1. [Cosmos-Predict1](https://github.com/nvidia-cosmos/cosmos-predict1) is a collection of general-purpose world foundation models for Physical AI that can be fine-tuned into customized world models for downstream applications. -2. [Cosmos-Transfer1](https://github.com/nvidia-cosmos/cosmos-transfer1) is a world-to-world transfer model designed to bridge the perceptual divide between simulated and real-world environments. -3. [Cosmos-Reason1](https://github.com/nvidia-cosmos/cosmos-reason1) models understand the physical common sense and generate appropriate embodied decisions in natural language through long chain-of-thought reasoning processes. -----------------------------------------------------------