---
title: TrajScene-60K
canonical_url: "https://www.modelscope.cn/datasets/YanranZhang/TrajScene-60K"
md_url: "https://www.modelscope.cn/datasets/YanranZhang/TrajScene-60K.md"
repository: YanranZhang/TrajScene-60K
chinese_name: TrajScene-60K
last_updated: 2026-07-06
license: apache-2.0
storage_size: "2.7 TB"
downloads: 1116
stars: 1
---

# TrajScene-60K

> TrajScene-60K - YanranZhang 在 ModelScope 开源的数据集。[ECCV 2026] MoGe4D: Geometry-Aware Single-Image 4D Synthesis via Dense Trajectory Generation

YanranZhang/TrajScene-60K 是 ModelScope 魔搭社区上的数据集，存储大小 2.7 TB，采用 apache-2.0 许可。

- **Repository**: YanranZhang/TrajScene-60K
- **License**: apache-2.0
- **Storage size**: 2.7 TB
- **Downloads**: 1116
- **Stars**: 1
- **Last updated**: 2026-07-06

Source: https://www.modelscope.cn/datasets/YanranZhang/TrajScene-60K

---

# [ECCV 2026] MoGe4D: Geometry-Aware Single-Image 4D Synthesis via Dense Trajectory Generation

<b>[Yanran Zhang](https://github.com/Zhangyr2022/)<sup>\*,1</sup>, [Ziyi Wang](https://wangzy22.github.io/)<sup>\*,1</sup>, [Wenzhao Zheng](https://wzzheng.net/#)<sup>†,1</sup>, [Zheng Zhu](http://www.zhengzhu.net/)<sup>2</sup>, [Jie Zhou](https://scholar.google.com/citations?user=6a79aPwAAAAJ&hl=en)<sup>1</sup>, [Jiwen Lu](https://ivg.au.tsinghua.edu.cn/Jiwen_Lu/)<sup>1</sup></b>

<sup>1</sup>Department of Automation, Tsinghua University &nbsp;&nbsp;&nbsp; <sup>2</sup>GigaAI

<i><sup>*</sup>Equal Contribution &nbsp;&nbsp; <sup>†</sup>Corresponding Author</i>

<p align="center">
  <a href="https://github.com/Zhangyr2022/MoGe4D"><img src="https://img.shields.io/badge/GitHub-Code-black?logo=github" alt="GitHub"></a>
  <a href="https://arxiv.org/abs/2512.05044"><img src="https://img.shields.io/badge/arXiv-Paper-b31b1b?logo=arxiv&logoColor=white" alt="arXiv"></a>
  <a href="https://ivg-yanranzhang.github.io/MoGe4D/"><img src="https://img.shields.io/badge/Project-Website-blue?logo=googlechrome&logoColor=white" alt="Project"></a>
  <br>
  <a href="https://www.modelscope.cn/models/YanranZhang/MoGe4D"><img src="https://img.shields.io/badge/🤖%20ModelScope-Model-4e29ff" alt="ModelScope Model"></a>
  <a href="https://huggingface.co/Yanran21/MoGe4D"><img src="https://img.shields.io/badge/🤗%20HuggingFace-Model-ffd21e" alt="HuggingFace Model"></a>
  <a href="https://www.modelscope.cn/datasets/YanranZhang/TrajScene-60K"><img src="https://img.shields.io/badge/🤖%20ModelScope-Dataset-4e29ff" alt="ModelScope Dataset"></a>
</p>

## 📄 Paper Summary

A robust 4D generative framework needs a multi-faceted dataset with three modalities: **dense 4D point trajectories**, **viewpoint-specific visual observations**, and **high-level semantic descriptions**. To address the acute scarcity of large-scale, high-quality 4D scene data with complex dynamics, we build **TrajScene-60K**.

It is curated from the [WebVid-10M](https://github.com/m-bain/webvid) corpus: ~200,000 candidates are extracted and filtered with VLM/LLM-based caption filtering (CogVLM2 & DeepSeek-V3), followed by scale-consistency and geometry validation. The result is a real-world, indoor/outdoor dataset with dense occlusion-aware tracking, per-frame depth, and language descriptions — significantly surpassing prior 4D datasets in scale and annotation richness.

## 📊 Statistics

| Statistic | Value |
|---|---|
| Samples | 60,000 videos |
| Total frames | 3M+ |
| 3D point annotations | ~12 billion (1.2 × 10¹⁰) |
| Resolution | 596 × 336, 49 frames |
| Source | WebVid-10M (VLM-filtered) |
| Annotations | dense 4D point trajectories · per-frame depth · occlusion masks · captions |
| Scene type | real-world, indoor & outdoor |

## 📁 Directory Structure

The dataset is shipped as range archives `<range>.tar.gz` (50 samples per range), covering all 60,000 samples (scenes `000001`–`060000`).

```
<range>.tar.gz                 # e.g. 000001_000050.tar.gz  (scenes 000001–000050)
└── <range>/
    ├── <id>.mp4                     # source video (ground-truth observation)
    ├── <id>.txt                     # caption (scene content + dynamic behavior)
    ├── <id>_dt3d_pred.pkl           # dense 4D point trajectories
    ├── <id>_dt3d_render.mp4         # rendered multi-view video
    ├── <id>_mask_render.mp4         # occlusion mask video
    └── <id>_mask_render_binary.npy  # binary occlusion mask
```

> Range archives are numbered continuously: `000001_000050`, `000051_000100`, …, `059951_060000`. A small number of ranges with no valid renderings are omitted.

## ⬇️ Download

```bash
pip install modelscope
modelscope download --dataset YanranZhang/TrajScene-60K --local_dir ./data/TrajScene-60K
```

Each archive is self-contained — you can download a subset of range archives for quick experiments.

## 🛠️ Usage

TrajScene-60K provides the supervision to train MoGe4D's components:
- **4D point trajectories (`*_dt3d_pred.pkl`)** + depth → train **4D-STraG** (trajectory generation with depth-guided motion normalization).
- **Rendered/mask videos** + source videos + captions → train **4D-ViSM** (novel-view synthesis).

See the [GitHub repository](https://github.com/Zhangyr2022/MoGe4D) (`scripts/4D_STraG_training`, `scripts/4D_ViSM_training`) for the data-loading and training pipeline.

## ⚖️ License & Disclaimer

Released under the Apache 2.0 license for research purposes. TrajScene-60K is derived from WebVid-10M using LLM/VLM-based filtering and therefore inherits the demographic and content biases of the source videos and filtering models. Users are responsible for complying with the original WebVid-10M license and any applicable regulations.

## 📖 Citation

```bibtex
@inproceedings{zhang2026moge4d,
  title={Geometry-Aware Single-Image 4D Synthesis via Dense Trajectory Generation},
  author={Zhang, Yanran and Wang, Ziyi and Zheng, Wenzhao and Zhu, Zheng and Zhou, Jie and Lu, Jiwen},
  booktitle={European Conference on Computer Vision (ECCV)},
  year={2026}
}
```

## 📧 Contact

- Yanran Zhang — zhangyr21@mails.tsinghua.edu.cn