

Videoworld
Overview :
VideoWorld is a deep generative model focused on learning complex knowledge from pure visual inputs (unlabelled videos). It explores how to learn task rules, reasoning, and planning abilities using only visual information through autoregressive video generation techniques. The model's core advantage lies in its innovative Latent Dynamic Model (LDM), which efficiently represents multi-step visual transformations, significantly enhancing learning efficiency and knowledge acquisition capability. VideoWorld performs exceptionally well in video Go and robotic control tasks, showcasing its strong generalization ability and capacity to learn complex tasks. The research background of this model is inspired by the way biological entities learn knowledge through vision rather than language, aiming to pave new pathways for knowledge acquisition in artificial intelligence.
Target Users :
This product is ideal for researchers and developers interested in artificial intelligence, computer vision, and robotic control, particularly those seeking to explore how to learn knowledge from unlabelled visual data. It is also suitable for developers of robotic and automation systems that require efficient knowledge acquisition and generalization capabilities.
Use Cases
In the video Go task, VideoWorld can play by generating the next board state.
In robotic control tasks, VideoWorld can control a robotic arm to perform various operations.
With the Latent Dynamic Model (LDM), VideoWorld can efficiently learn and reason about complex visual tasks.
Features
Learn task rules and operations through an autoregressive video generation model.
Efficiently represent multi-step visual transformations using the Latent Dynamic Model (LDM).
Achieve a professional level of 5-dan in video Go tasks.
Enable cross-environment generalization in robotic control tasks.
Provide open-source code and data to support further research.
How to Use
1. Visit the project homepage to download the open-source code and data.
2. Use VQ-VAE to convert video frames into discrete tokens.
3. Train an autoregressive Transformer model using a next-frame prediction paradigm.
4. During the testing phase, the model generates new frames based on the previous frame and extracts task operations from them.
5. Apply the Latent Dynamic Model (LDM) to enhance learning efficiency and performance.
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