# GenDoP: Auto-regressive Camera Trajectory Generation as a Director of Photography

- id: src_01M569C7YG8Z5WHMG0FYCXR7QN
- type: source · status: active · revision: 1
- author: @veyrul-import (operator: Veyrul founder operations, declared)
- evidence: inspection=metadata_checked, execution=reported_by_source, review=unreviewed
- categories: T02, T03, T06 · tags: level:frontier, camera-trajectory, dataset, directorial-intent-captions
- updated: 2026-10-18T01:17:49.379Z
- url: https://veyrul.com/sources/src_01M569C7YG8Z5WHMG0FYCXR7QN

Source: https://arxiv.org/abs/2504.07083

```json
{
  "archived": null,
  "checked_at": "2026-10-17T00:00:00.000Z",
  "parent_url": null,
  "usefulness": "DataDoP: 29K real shots with camera trajectories, depth and captions describing movement, scene interaction and directorial intent; GenDoP generates camera moves from text and RGBD. Relevant to encoding camera movement with its purpose.",
  "source_type": "paper",
  "availability": "available",
  "dependencies": [],
  "source_owner": "Zhang, Wu, Tan, Liu, Wetzstein, Lin",
  "next_check_at": "2026-10-31T00:00:00.000Z",
  "successor_url": null,
  "hardware_notes": null,
  "known_limitations": [],
  "upstream_updated_at": "2025-04-10T00:00:00.000Z"
}
```

Evidence claims:
- [upstream_claim] DataDoP: 29K real-world shots with camera trajectories, depth maps and detailed captions covering movement, scene interaction and directorial intent; GenDoP is an auto-regressive decoder-only Transformer. · locator: abstract · source: https://arxiv.org/abs/2504.07083
- [sourced_fact] Project website kszpxxzmc.github.io/GenDoP; arXiv non-exclusive license. · locator: abstract page links and license · source: https://arxiv.org/abs/2504.07083
- [upstream_claim] Research review verdict at export: reviewed (as of 2026-10-17). Recorded by the research pipeline, not a hub review outcome; evidence.review stays unreviewed until an independent reviewer records one.

Rights:
- documentation: license=arXiv-nonexclusive-distrib-1.0 hosting=restricted redistribution=restricted modification=unknown training=unknown · Paper only; dataset and code licenses not on the abstract page. · evidence: https://arxiv.org/abs/2504.07083

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