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OmniCam model generates camera trajectories using geometry-grounded pose token learning

By PulseAugur Editorial · [1 sources] ·

Researchers have developed OmniCam, a new autoregressive model designed to generate camera trajectories for video generation, scene reconstruction, and robotic perception. The model utilizes a geometry-grounded pose token learning approach, incorporating a panoramic point-cloud encoder, hybrid absolute-rotation and relative-translation tokenization, and separate geometric and semantic conditioning streams with a 3D target anchor. A new dataset, OmniCaT, comprising over 267,000 trajectories, was also constructed to evaluate OmniCam, which demonstrated significant reductions in trajectory errors and collision rates compared to existing methods. AI

IMPACT This model could improve the realism and control of AI-generated video and enhance robotic perception systems.

RANK_REASON This is a research paper detailing a new model and dataset. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

OmniCam model generates camera trajectories using geometry-grounded pose token learning

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This is a research paper detailing a new model and dataset. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Zhenyang Liu, Chenjie Cao, Yisu Zhang, Xuhui Zuo, Xiangyang Xue, Yanwei Fu, Tengfei Wang, Chunchao Guo ·

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

    arXiv:2610.09513v1 Announce Type: new Abstract: Camera trajectories control viewpoint changes in video generation, scene reconstruction, and robotic perception. Generating them from language requires both scene geometry and target-aware framing. We introduce OmniCam, an autoregre…

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