OmniCam: Omni-Camera Trajectory Generation via Geometry-Grounded Pose Token Learning

arXiv 2026 23.1 method

TLDR

OmniCam generates camera pose sequences from a single panorama and text using geometry-grounded pose token learning, reducing trajectory errors and collisions.

Reasoning

The paper presents a clear method with strong empirical validation on a new large-scale dataset and downstream tasks. However, the core contribution is trajectory generation, not world modeling, so relevance to the requested keywords is minimal.

Read-first score

Read-first score 23.1, weighted from topical fit, citation, graph, method, reproducibility, and recency signals. Original total remains 3.

Recency 5%

100

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

Methodology quality 15%

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

Reproducibility 15%

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

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%

4.3

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

FrontierMethodology anchor

Rank sensitivity

Stability: volatile; rank range: 196.

Keyword Scores

world model

video world model

world dynamics prediction

world simulator

generative world model

interactive world model

model-based reinforcement learning world model

Resources

Paper