Technology Category
Computer Vision: Video Understanding & Activity AnalysisMachine Learning: Large Multimodal Models (LMMs)Humans and AI: Human-Aware Planning and Behavior Prediction
VideoWorld: Exploring Knowledge Learning from Unlabeled Videos
🤖 AI Summary
This work investigates a self-supervised learning paradigm driven solely by unlabeled video data, aiming to master complex tasks—such as Go and robotic control—without reward signals or explicit search strategies. We propose VideoWorld: an end-to-end autoregressive video generation framework featuring a novel Latent Dynamics Model (LDM) that explicitly captures spatiotemporal causal transitions in visual streams, integrated with unsupervised spatiotemporal representation learning and video-conditioned policy distillation. Our key contribution is the first empirical demonstration that raw video streams inherently encode sufficient semantic and dynamical knowledge to support rule understanding, reasoning, and planning. On Video-GoBench, a 300M-parameter VideoWorld model achieves professional 5-dan level performance—without search or reward. On CALVIN and RLBench, it approaches oracle-level performance and exhibits strong cross-environment generalization.
Technology Category
Application Category
📝 Abstract
This work explores whether a deep generative model can learn complex knowledge solely from visual input, in contrast to the prevalent focus on text-based models like large language models (LLMs). We develop VideoWorld, an auto-regressive video generation model trained on unlabeled video data, and test its knowledge acquisition abilities in video-based Go and robotic control tasks. Our experiments reveal two key findings: (1) video-only training provides sufficient information for learning knowledge, including rules, reasoning and planning capabilities, and (2) the representation of visual change is crucial for knowledge acquisition. To improve both the efficiency and efficacy of this process, we introduce the Latent Dynamics Model (LDM) as a key component of VideoWorld. Remarkably, VideoWorld reaches a 5-dan professional level in the Video-GoBench with just a 300-million-parameter model, without relying on search algorithms or reward mechanisms typical in reinforcement learning. In robotic tasks, VideoWorld effectively learns diverse control operations and generalizes across environments, approaching the performance of oracle models in CALVIN and RLBench. This study opens new avenues for knowledge acquisition from visual data, with all code, data, and models open-sourced for further research.
Problem
Research questions and friction points this paper is trying to address.
Self-supervised Learning
Unlabeled Video Data
Complex Task Mastery
Innovation
Methods, ideas, or system contributions that make the work stand out.
VideoWorld model
LDM component
unsupervised learning
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