LEGO: A Lifting-Free Approach for Exocentric-to-Egocentric Video Generation

arXiv 2026 16.1 method

TLDR

Proposes a lifting-free exocentric-to-egocentric video generation method using a learned LVSM-style view synthesizer that conditions a diffusion generator without depth or point clouds.

Reasoning

The paper introduces a novel condition design for egocentric video generation, avoiding depth-based lifting and leveraging learned correspondence distributions and confidence masking. However, the visible abstract lacks explicit real-world evaluation details, and its connection to broader world-model research is indirect.

Read-first score

Read-first score 16.1, weighted from topical fit, citation, graph, method, reproducibility, and recency signals. Original total remains 0.

Recency 5%

100

Uses a gentle age decay so recent papers surface without erasing older foundations. 2026

Reproducibility 15%

Screens links and visible text for paper, code, dataset, artifact, and repository signals. pdf=True; code=False; dataset=False; markers=dataset

Methodology quality 15%

Screens visible abstract and analysis fields for experiment, dataset, baseline, metric, and limitation evidence. markers=dataset

Graph prestige 15%

5.7

PageRank-style prestige over the local citation graph built from available OpenAlex references. local_pagerank=0.0016

Topical relevance 25%

Uses existing LLM keyword relevance scores normalized to 0-100. world model,world simulator,generative world model,interactive world model,video world model,world dynamics prediction,model-based reinforcement learning world model

Citation impact 15%

Uses OpenAlex-shaped citation metadata as a bibliometric attention signal, separate from paper quality. cited_by_count=0

Citation velocity 10%

Citation velocity estimates citations per publication-year to reduce old-paper bias. velocity=0.00

Field roles

Frontier

Rank sensitivity

Stability: volatile; rank range: 40.

Keyword Scores

world model

world simulator

generative world model

interactive world model

video world model

world dynamics prediction

model-based reinforcement learning world model

Resources

Paper