TVR-SFT-VA

Visual-Action SFT training data for Target Viewpoint Reproduction (TVR), from the paper "Where to Look: Can Foundation Models Reach a Target Viewpoint Through Active Exploration?" [arXiv].

Dataset Description

Expert trajectories collected by a rule-based Dijkstra planner in AI2-THOR indoor environments. Each trajectory is a multi-turn conversation where the agent observes its current view and the target view, then takes an action to reduce the viewpoint discrepancy.

• Samples: 1,600 trajectories (multi-turn, average ~14 turns each)
• Images: 22,335 frames (640×360, with CURRENT/TARGET labels burned in)
• Scenes: 40 scenes (20 iTHOR single-room + 20 ProcTHOR multi-room)
• Format: LLaMA-Factory ShareGPT

File Structure

.
├── sft_train.json       # ShareGPT format (system + conversations + images)
├── dataset_info.json    # LLaMA-Factory dataset registration
└── images/              # 22,335 PNG files organized by task_id
    ├── {task_id}/
    │   ├── target.png
    │   ├── step_00.png
    │   ├── step_01.png
    │   └── ...
    └── ...

Sample Format

Each sample in sft_train.json:

{
    "system": "You are a navigation agent in an indoor environment...",
    "conversations": [
        {"from": "human", "value": "Your CURRENT observation:\n<image>\nTARGET viewpoint...\n<image>\nValid actions: ..."},
        {"from": "gpt", "value": "Action: RotateRight"},
        {"from": "human", "value": "Your CURRENT observation:\n<image>\nTARGET viewpoint...\n<image>\nValid actions: ..."},
        {"from": "gpt", "value": "Action: MoveAhead"},
        ...
        {"from": "gpt", "value": "Action: Stop"}
    ],
    "images": ["images/{task_id}/step_00.png", "images/{task_id}/target.png", ...]
}

Usage

See the TVRBench repository for training configs and full instructions.

Citation

@misc{li2026lookfoundationmodelsreach,
      title={Where to Look: Can Foundation Models Reach a Target Viewpoint Through Active Exploration?}, 
      author={Liyang Li and Muzhi Zhu and Zhiyue Zhao and Hengyu Zhao and Ke Liu and Linhao Zhong and Hao Chen and Chunhua Shen},
      year={2026},
      eprint={2606.01247},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2606.01247}, 
}