world-models-video-prediction-robotics

name	world-models-video-prediction-robotics
title	World Simulation with Video Foundation Models for Physical AI
version	0.0.2
engine	skillxiv-v0.0.2-claude-opus-4.6
license	MIT
url	https://arxiv.org/abs/2511.00062
keywords	["World Models","Video Generation","Robotics","Physical Simulation","Unified Multimodal"]
description	Build unified world models using flow-based video generation architecture that handles Text2World, Image2World, and Video2World in a single model, trained on 200M video clips with RL post-training for improved instruction-following and video quality.

Title: Generate Realistic Video Worlds From Multiple Input Modalities

Robotics and embodied AI require understanding how the physical world evolves. Cosmos-Predict is a unified world model that generates videos from text descriptions, images, or previous video clips using a flow-based architecture. The model operates at 1280×720 resolution, handles variable-length sequences, and grounds generation in physical reality through alignment with a physics-aware vision-language model.

The key innovation is unifying multiple input modalities (text, image, video) into a single generative framework, enabling flexible control of world simulation.

Core Concept

Unified Multimodal World Simulation:

• Single Flow-Based Model: Text2World, Image2World, Video2World share architecture
• Conditional Generation: Visual or textual conditions ground video generation
• Physics-Aware Grounding: Cosmos-Reason vision-language model provides semantic understanding
• Large-Scale Training: 200M curated video clips for diverse world dynamics
• RL Post-Training: Policy optimization for instruction-following and quality metrics

Architecture Overview

• Flow-Based Generator: Diffusion-style denoising for video generation
• Multimodal Conditioning: Text encoders (T5/LLM) + image encoders (ViT) + video encoders
• Vision-Language Grounding: Cosmos-Reason model for semantic grounding and physical understanding
• Resolution: 1280×720 with variable frame counts (typically 16-128 frames)
• Training Scale: 200M video clips, multi-GPU pre-training, RL refinement

Implementation Steps

1. Design Flow-Based Video Generator

Implement the core generative model using flow matching.

class CosmosFlowVideoGenerator(nn.Module):
    def __init__(self, hidden_dim=2048, num_blocks=24):
        self.hidden_dim = hidden_dim

        # Video encoder/decoder using 3D convolutions
        self.video_encoder = nn.Sequential(
            nn.Conv3d(, , kernel_size=(, , ), stride=(, , ), padding=(, , )),
            nn.ReLU(),
            nn.Conv3d(, , kernel_size=(, , ), stride=(, , ), padding=(, , )),
            nn.ReLU()
        )

        
        .transformer = nn.ModuleList([
            nn.TransformerEncoderLayer(d_model=hidden_dim, nhead=, batch_first=)
             _  (num_blocks)
        ])

        .video_decoder = nn.Sequential(
            nn.ConvTranspose3d(, , kernel_size=(, , ), stride=(, , ), padding=(, , )),
            nn.ReLU(),
            nn.ConvTranspose3d(, , kernel_size=(, , ), stride=(, , ), padding=(, , ))
        )

     ():
        
        
        video = video.permute(, , , , )
        latent = .video_encoder(video)
         latent

     ():
        
        video = .video_decoder(latent)
        
        video = video.permute(, , , , )
         video

     ():
        
        latent = .encode_video(video)
        batch, channels, frames, h, w = latent.shape

        
        latent_flat = latent.permute(, , , , ).reshape(batch * frames * h * w, channels)

        
         block  .transformer:
            latent_flat = block(latent_flat.unsqueeze()).squeeze()

        
        latent = latent_flat.reshape(batch, frames, h, w, channels).permute(, , , , )

        
        video_recon = .decode_latent(latent)
         video_recon