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OmniCam
ZhenyangLiu/OmniCam
OmniCam is a other model from ZhenyangLiu. Use it for the other task on the model card, and read the license before you ship it in a product. It is set up for safetensors. The card lists the license as cc-by-nc-4.0.
Pretrained weights for OmniCam: Omni-Camera Trajectory Generation via Geometry-Grounded Pose Token Learning.
Access
Public
Updated Aug 5, 2026
Repo size
10.9 GB
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.safetensors10.9 GB · 100%
At a glance
- Task
- other
- Library
- safetensors
- License
- cc-by-nc-4.0
- Access
- Public
- Created
- Aug 5, 2026
- Updated
- Aug 5, 2026
- SHA
- 3ee8e89d
Model card
From the Hugging Face model README
OmniCam — Pretrained Checkpoints
Pretrained weights for OmniCam: Omni-Camera Trajectory Generation via Geometry-Grounded Pose Token Learning.
Given a single 360° panorama and a textual trajectory description, OmniCam autoregressively generates a spatially aware SE(3) camera-pose sequence, supporting four camera behaviors — Target, Surround, Reconstruct, Wander.
- 💻 Code, dataset pipeline & evaluation benchmark: https://github.com/ZhenyangLiu/OmniCam
- 🌐 Project page: https://zhenyangliu.github.io/OmniCam/
Files
| File | Selected by | Size |
|---|---|---|
best_ate.safetensors | best trajectory accuracy (ATE) on held-out scenes | 5.1 GB |
best_loss.safetensors | lowest validation loss | 5.1 GB |
We recommend best_ate.safetensors for trajectory-quality evaluation.
Usage
pip install huggingface_hub
huggingface-cli download ZhenyangLiu/OmniCam best_ate.safetensors --local-dir checkpoints
# in the OmniCam repo
cd omnicam
RESUME_PATH=../checkpoints/best_ate.safetensors bash scripts/eval.sh
Citation
@inproceedings{omnicam2026,
title = {OmniCam: Omni-Camera Trajectory Generation via
Geometry-Grounded Pose Token Learning},
author = {Anonymous Authors},
booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
year = {2026}
}
- Task
- other
- Library
- safetensors
- License
- cc-by-nc-4.0
- Created
- Aug 5, 2026
- Updated
- Aug 5, 2026