LIFT-Vista is a dataset with large camera viewpoint changes and joint camera-layout annotations.
## Overview
| | `camera.csv` | `camlayout.csv` |
|---|---|---|
| Clips | 120,898 | 58,272 (a subset of the camera clips) |
| Annotations | camera trajectory, caption | camera trajectory, caption, last-frame layout, per-frame box tracks |
Every clip has 81 frames at 16 fps (about 5 s) at the native resolution of its source video (98% are 1280×720),
encoded with H.264. The release is about 413 GB, of which 407 GB are videos.
## Download
Videos and annotations are stored as tar shards in `tars/` (about 4 GB per video shard). Extracting them in the
dataset root restores the `videos/` and `annotations/` directories that the CSV paths point to (413 GB in total):
```bash
hf download Overdog/LIFT-Vista --repo-type dataset --local-dir LIFT-Vista
cd LIFT-Vista
for f in tars/*.tar; do tar -xf "$f"; done
```
`camlayout.csv` only needs the `camlayout` shards (179 GB):
```bash
hf download Overdog/LIFT-Vista --repo-type dataset --local-dir LIFT-Vista \
--include "*.md" --include "*.csv" --include "assets/*" \
--include "tars/annotations_camlayout.tar" --include "tars/videos_camlayout_*.tar"
cd LIFT-Vista
for f in tars/*.tar; do tar -xf "$f"; done
```
## Structure
```
LIFT-Vista/
├── camera.csv
├── camlayout.csv
├── videos///.mp4
└── annotations////
├── camera_da3.npz # every clip
├── caption.txt # every clip
├── layout_lastframe.json # camlayout clips only
└── layout_track_x.json # camlayout clips only
```
`` is `SpatialVID`, `Sekai` or `RealEstate10K`, `` is the id of the video in the source dataset,
and `` is the window index `w` of the 81-frame window extracted from the source video.
### CSV columns
| Column | Description |
|---|---|
| `UID` | unique clip id, `_` |
| `resolution` | clip resolution, `WxH` |
| `fps`, `num_frames` | frame rate and frame count of the **source video** the clip was cut from (every clip itself has 81 frames at 16 fps) |
| `Video_Path` | the clip, relative to the dataset root |
| `Annotation_Path` | the annotation directory, relative to the dataset root |
| `CameraFile` | camera file inside `Annotation_Path` |
| `layout_file` | last-frame layout inside `Annotation_Path` (`camlayout.csv` only) |
| `sam3_track_file` | box tracks inside `Annotation_Path` (`camlayout.csv` only) |
| `sam3_track_resolution` | pixel grid `WxH` of the track boxes; equal to `resolution` except for 183 clips whose tracks were computed at 640×352 or 832×480 (rescale x and y separately) |
| `caption` | text caption, identical to `caption.txt` |
### Camera (`camera_da3.npz`)
- `extrinsic`: `(81, 3, 4)` world-to-camera matrices `[R | t]`, OpenCV convention (x right, y down, z forward).
- `intrinsic`: `(81, 3, 3)` pinhole intrinsics in pixels of the clip resolution.
### Last-frame layout (`layout_lastframe.json`)
```
{
"instances": [
{
"id": 0,
"category": "chair",
"bbox": [0, 515, 332, 719],
"caption": "white upholstered chair with an orange throw pillow"
},
...
]
}
```
Salient foreground objects of the last frame (frame 80), with boxes `[x1, y1, x2, y2]` in pixels of the clip
resolution.
### Box tracks (`layout_track_x.json`)
```
{
"source_layout_file": "layout_lastframe.json",
"track_resolution": "1280x720", # pixel grid of every box in this file
"prompt_frame_idx": 80,
"num_frames": 81,
"tracks": {
"0": {"id": 0, "category": "chair", "caption": "...",
"bboxes": {"37": [x1, y1, x2, y2], ..., "80": [x1, y1, x2, y2]}}, # no box in frames 0..36
...
},
"frames": {"37": [{"id": 0, "category": "chair", "caption": "...", "bbox": [x1, y1, x2, y2]}, ...], ...}
}
```
## Statistics
`camera.csv` has **120,898** clips for camera-control training (Stage 1 in the paper) and
`camlayout.csv` has a subset of **58,272** clips with layouts (Stages 2 and 3).
The layout annotations contain **5.5 objects per clip** on average and cover **8,302 category labels**, including indoor objects (chair, window, lamp, cabinet, sofa, ...) and outdoor objects (person, building, car, boat, tree, sign, ...).
**84.8%** of the layout clips contain objects that are
not visible in the first frame and only appear in later views.
(a, b) Distributions of the FoV expansion ratio and the translation distance over all candidate windows before
filtering (blue) and ours (red); filtering removes the mass of near-static windows and shifts both
metrics toward larger viewpoint changes.
(c) Joint distribution of the two camera-motion metrics, with 50% and 90% mass contours per source dataset.
(d) Joint distribution of the two content change ratios; the two directions are correlated and complementary but not redundant.
(e) Number of training clips per source dataset.
**FoV expansion ratio.** We uniformly sample $K$ keyframes from each clip ($K = 8$). For the $i$-th keyframe, let
$\Omega_{k_i} \subseteq \mathbb{S}^2$ denote the set of visible viewing directions in a common world coordinate system;
its spherical area is estimated using uniformly sampled directions on the unit sphere. The FoV expansion ratio
$$
r_{\mathrm{FoV}} = \frac{\left| \bigcup_{i=1}^{K} \Omega_{k_i} \right|}{\left| \Omega_{k_1} \right|}
$$
measures the total viewing region covered by the clip relative to the first frame.
**Translation distance.** The accumulated camera translation is
$$
d_{\mathrm{trans}} = \sum_{i=1}^{K-1} \left\| \mathbf{o}_{k_{i+1}} - \mathbf{o}_{k_i} \right\|_2 ,
$$
where $\mathbf{o}_{k_i}$ denotes the camera origin of the $i$-th keyframe.
**Content change ratios (CCR).** Since camera motion alone does not directly measure changes in visible scene content,
we additionally compute a patch-level CCR between the first and last frames using DINOv2. Let
$\{\mathbf{p}_i\}_{i=1}^{N}$ and $\{\mathbf{q}_j\}_{j=1}^{N}$ denote their $\ell_2$-normalized patch embeddings. The
last-frame CCR is
$$
r_{lf\text{-}CCR} = \frac{1}{N} \sum_{j=1}^{N} \mathbb{I}\left[ \max_i \langle \mathbf{p}_i, \mathbf{q}_j \rangle < \tau \right],
$$
where $\mathbb{I}[\cdot]$ denotes the indicator function, $\langle \cdot, \cdot \rangle$ denotes the cosine similarity,
and $\tau$ is a similarity threshold ($\tau = 0.5$). $r_{lf\text{-}CCR}$ measures the fraction of last-frame patches
unmatched in the first frame (new content); $r_{ff\text{-}CCR}$ is computed analogously in the reverse direction to
measure content leaving the initial view (lost content).
## Usage
```python
import json, os
import numpy as np
import pandas as pd
root = "LIFT-Vista"
df = pd.read_csv(os.path.join(root, "camlayout.csv"))
row = df.iloc[0]
video_path = os.path.join(root, row.Video_Path) # 81 frames, 16 fps
ann_dir = os.path.join(root, row.Annotation_Path)
cam = np.load(os.path.join(ann_dir, row.CameraFile))
w2c, K = cam["extrinsic"], cam["intrinsic"] # (81, 3, 4), (81, 3, 3)
with open(os.path.join(ann_dir, row.sam3_track_file)) as f:
tracks = json.load(f)["tracks"]
tw, th = map(int, row.sam3_track_resolution.split("x")) # pixel grid of the boxes
for obj in tracks.values():
frames = sorted(int(k) for k in obj["bboxes"])
print(obj["category"], "| visible in frames", frames[0], "to", frames[-1], "|", obj["caption"])
```
The LIFT training code reads these CSVs directly; see the [code repository](https://github.com/jsxzs/LIFT).
## Citation
Coming soon.
Please also cite the source datasets:
```bibtex
@misc{wang2025spatialvidlargescalevideodataset,
title={SpatialVID: A Large-Scale Video Dataset with Spatial Annotations},
author={Jiahao Wang and Yufeng Yuan and Rujie Zheng and Youtian Lin and Jian Gao and Lin-Zhuo Chen and Yajie Bao and Yi Zhang and Chang Zeng and Yanxi Zhou and Xiaoxiao Long and Hao Zhu and Zhaoxiang Zhang and Xun Cao and Yao Yao},
year={2025},
eprint={2509.09676},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2509.09676},
}
@article{li2025sekai,
title={Sekai: A Video Dataset towards World Exploration},
author={Zhen Li and Chuanhao Li and Xiaofeng Mao and Shaoheng Lin and Ming Li and Shitian Zhao and Zhaopan Xu and Xinyue Li and Yukang Feng and Jianwen Sun and Zizhen Li and Fanrui Zhang and Jiaxin Ai and Zhixiang Wang and Yuwei Wu and Tong He and Jiangmiao Pang and Yu Qiao and Yunde Jia and Kaipeng Zhang},
journal={arXiv preprint arXiv:2506.15675},
year={2025}
}
@article{zhou2018stereo,
author = {Zhou, Tinghui and Tucker, Richard and Flynn, John and Fyffe, Graham and Snavely, Noah},
title = {Stereo Magnification: Learning View Synthesis using Multiplane Images},
journal = {ACM Transactions on Graphics (SIGGRAPH)},
volume = {37},
number = {4},
articleno = {65},
year = {2018},
url = {https://dl.acm.org/doi/10.1145/3197517.3201323}
}
```
