---
license: apache-2.0
language:
  - en
tags:
  - embodied-ai
  - spatial-intelligence
  - tvr
task_categories:
  - image-text-to-text
size_categories:
  - 1K<n<10K
arxiv: 2606.01247
---

# 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]](https://arxiv.org/abs/2606.01247).

## Dataset Description

Expert trajectories collected by a rule-based Dijkstra planner in [AI2-THOR](https://ai2thor.allenai.org/) 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](https://github.com/hiyouga/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`:

```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](https://github.com/aim-uofa/TVRBench) for training configs and full instructions.

## Citation

```bibtex
@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}, 
}
```