Datasets:

zxyun
/

PKU-DyMVHumans

@@ -1,100 +1,80 @@
 ---
-license: c-uda
 language:
-- en
-- zh
-tags:
-- videos
-size_categories:
-- 100M<n<1B
----
-# PKU-DyMVHumans
-## Sources
-Project page：https://pku-dymvhumans.github.io/
-Github: https://github.com/zhengxyun/PKU-DyMVHumans
-Paper: https://arxiv.org/abs/2403.16080
 ## Overview
-PKU-DyMVHumans is a versatile human-centric dataset designed for high-fidelity reconstruction and rendering of dynamic human performances in markerless multi-view capture settings.
-It comprises 32 humans across 45 different dynamic scenarios, each featuring highly detailed appearances and complex human motions.
-## Key Features
-- **High-fidelity performance**：We construct a multi-view system to capture humans in motion, containing 56/60 synchronous cameras with 1080P or 4K resolution
-- **High-detailed appearance**: It captures complex cloth deformation, and intricate texture details, like delicate satin ribbon and special headwear.
-- **Complex human motion**: It covers a wide range of special costume performances, artistic movements, and sports activities.
-- **Human-object/scene interactions**: It includes human-object interactions, multi-person interactions and complex scene effects (like smoking).
-## Data Details
-### Download Instructions
-Part1: It contains 8 scenarios, which can be directly used for benchmarks in [NeuS](https://github.com/Totoro97/NeuS), [NeuS2](https://github.com/19reborn/NeuS2), [Instant-ngp](https://github.com/NVlabs/instant-ngp), and [3D-Gaussian](https://github.com/graphdeco-inria/gaussian-splatting)).
-Part2: It contains 37 scenarios with the video sequences of the dataset.
-Note that by downloading the datasets, you acknowledge that you have read the agreement, understand it, and agree to be bound by them:
-- The PKU-DyMVHumans dataset is made available only for non-commercial research purposes. Any other use, in particular any use for commercial purposes, is prohibited.
-- You agree not to further copy, publish or distribute any portion of the dataset.
-- Peking University reserves the right to terminate your access to the dataset at any time.
-### Dataset Structure
-For each scene, we provide the multi-view images (`./case_name/per_view/cam_*/images/`), the coarse foreground with RGBA channels (`./case_name/per_view/cam_*/images/`), as well as the coarse foreground segmentation (`./case_name/per_view/cam_*/pha/`), which are obtained using [BackgroundMattingV2](https://github.com/PeterL1n/BackgroundMattingV2).
-To make the benchmarks easier compare with our dataset, we save different data formats (i.e., [Surface-SOS](https://github.com/zhengxyun/Surface-SOS), [NeuS](https://github.com/Totoro97/NeuS), [NeuS2](https://github.com/19reborn/NeuS2), [Instant-ngp](https://github.com/NVlabs/instant-ngp), and [3D-Gaussian](https://github.com/graphdeco-inria/gaussian-splatting)) of PKU-DyMVHumans at **Part1** and write a document that describes the data process.
 ```
-.
-|--- <case_name>
-|   |--- cams
-|   |--- videos
-|   |--- per_view
-|   |--- per_frame
-|   |--- data_ngp
-|   |--- data_NeuS
-|   |--- data_NeuS2
-|   |--- data_COLMAP
-|   |--- <overview_fme_*.png>
-|--- ...
-```
-## Benchmark
-The objective of our benchmark is to achieve robust geometry reconstruction and novel view synthesis for dynamic humans under markerless and fixed multi-view camera settings, while minimizing the need for manual annotation and reducing time costs.
-This includes **neural scene decomposition**, **novel view synthesis**, and **dynamic human modeling**.
-## Citation
-If you find this repo is helpful, please cite:
-```
-@article{zheng2024PKU-DyMVHumans,
-  title={PKU-DyMVHumans: A Multi-View Video Benchmark for High-Fidelity Dynamic Human Modeling},
-  author={Zheng, Xiaoyun and Liao, Liwei and Li, Xufeng and Jiao, Jianbo and Wang, Rongjie and Gao, Feng and Wang, Shiqi and Wang, Ronggang},
-  journal={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
   year={2024}
 }
 ```

 ---
+# Example metadata to be added to a dataset card.
+# Full dataset card template at https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md
 language:
+  - en
+  - zh
+  - fr
+  - ja
+  - es
+license: cc-by-nc-sa-4.0
+tags:
+  - Multimedia
+  - Panoramic
+  - Video
+  - Multi-viewpoint
+viewer: false
+---
+# <i>360+x</i> Dataset
 ## Overview
+360+x dataset introduces a unique panoptic perspective to scene understanding, differentiating itself from traditional
+datasets by offering multiple viewpoints and modalities, captured from a variety of scenes
+### Key Features:
+- **Multi-viewpoint Captures:** Includes 360° panoramic video, third-person front view video, egocentric monocular
+  video, and egocentric binocular video.
+- **Rich Audio Modalities:** Features normal audio and directional binaural delay.
+- **2,152 multi-model videos**  captured by 360 cameras and Spectacles camera (8579k frames in total) Captured in 17
+  cities across 5 countries, covering 28 scenes ranging from Artistic Spaces to Natural Landscapes.
+- **Action Temporal Segmentation:** Provides labels for 38 action instances for each video pair.
+## Dataset Details
+### Project Description
+- **Developed by:** Hao Chen, Yuqi Hou, Chenyuan Qu, Irene Testini, Xiaohan Hong, Jianbo Jiao
+- **Funded by:** the Ramsay Research Fund, and the Royal Society Short Industry Fellowship
+- **License:** Creative Commons Attribution-NonCommercial-ShareAlike 4.0
+### Sources
+- **Repository:** Coming Soon
+- **Paper:** https://arxiv.org/abs/2404.00989
+## Dataset Statistics
+- **Total Videos:** 2,152, split between 464 videos captured using 360 cameras and 1,688 with Spectacles cameras.
+- **Scenes:** 15 indoor and 13 outdoor, totaling 28 scene categories.
+- **Short Clips:** The videos have been segmented into 1,380 shorter clips, each approximately 10 seconds long, totaling
+  around 67.78 hours.
+- **Frames:** 8,579k frames across all clips.
+## Dataset Structure
+Our dataset offers a comprehensive collection of panoramic videos, binocular videos, and third-person videos, each pair
+of videos accompanied by annotations. Additionally, it includes features extracted using I3D, VGGish, and ResNet-18.
+Given the high-resolution nature of our dataset (5760x2880 for panoramic and binocular videos, 1920x1080 for
+third-person front view videos), the overall size is considerably large. To accommodate diverse research needs and
+computational resources, we also provide a lower-resolution version of the dataset (640x320 for panoramic and binocular
+videos, 569x320 for third-person front view videos) available for download.
+<b>In this repo, we provide the lower-resolution version of the dataset. To access the high-resolution version, please
+visit the <a href="https://x360dataset.github.io/">official website</a>.</b>
+## BibTeX
 ```
+@inproceedings{chen2024x360,
+  title={360+x: A Panoptic Multi-modal Scene Understanding Dataset},
+  author={Chen, Hao and Hou, Yuqi and Qu, Chenyuan and Testini, Irene and Hong, Xiaohan and Jiao, Jianbo},
+  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
   year={2024}
 }
 ```