Datasets:

Eric-maadaa
/

ReCamMaster_REAL

	---
	license: cc-by-sa-4.0
	---
	# recammaster_based_multi_camera_video_dataset
	A Real-World Multi-Camera Video Dataset for Generative Vision AI

	## 📌 Overview
	The MultiScene360 Dataset is designed to advance generative vision AI by providing synchronized multi-camera footage from real-world environments.

	💡 Key Applications:
	✔ Video generation & view synthesis
	✔ 3D reconstruction & neural rendering
	✔ Digital human animation systems
	✔ Virtual/augmented reality development

	## 📊 Dataset Specifications (Public Version)
	\| Category \| Specification \|
	\|---------------------\|----------------------------------------\|
	\| Scenes \| 10 base + 3 extended scenes \|
	\| Scene Duration \| 10-20 seconds each \|
	\| Camera Views \| 4 synchronized angles per scene \|
	\| Total Video Clips \| ~144 \|
	\| Data Volume \| 20-30GB (1080p@30fps) \|

	Commercial version available with 200+ scenes and 6-8 camera angles

	## 🌆 Complete Scene Specification
	\| ID \| Environment \| Location \| Primary Action \| Special Features \|
	\|------\|-------------\|-----------------\|----------------------------\|---------------------------\|
	\| S001 \| Indoor \| Living Room \| Walk → Sit \| Occlusion handling \|
	\| S002 \| Indoor \| Kitchen \| Pour water + Open cabinet \| Fine hand motions \|
	\| S003 \| Indoor \| Corridor \| Walk → Turn \| Depth perception \|
	\| S004 \| Indoor \| Desk \| Type → Head turn \| Upper body motions \|
	\| S005 \| Outdoor \| Park \| Walk → Sit (bench) \| Natural lighting \|
	\| S006 \| Outdoor \| Street \| Walk → Stop → Phone check \| Gait variation \|
	\| S007 \| Outdoor \| Staircase \| Ascend stairs \| Vertical movement \|
	\| S008 \| Indoor \| Corridor \| Two people passing \| Multi-person occlusion \|
	\| S009 \| Indoor \| Mirror \| Dressing + mirror view \| Reflection surfaces \|
	\| S010 \| Indoor \| Empty room \| Dance movements \| Full-body dynamics \|
	\| S011 \| Indoor \| Window \| Phone call + clothes adjust\| Silhouette + semi-reflections \|
	\| S012 \| Outdoor \| Shopping street \| Walking + window browsing \| Transparent surfaces + crowd \|
	\| S013 \| Indoor \| Night corridor \| Walking + light switching \| Low-light adaptation \|

	## 🎥 Camera Configuration
	Physical Setup:
	cam01──────cam02
	\ /
	Subject
	/ \
	cam04──────cam03

	Technical Details:
	- Cameras: DJI Osmo Action 5 Pro (4 identical units)
	- Mounting: Tripod-stabilized at ~1.5m height
	- Distance: 2-3m from subject center
	- FOV Overlap: 20-30% between adjacent cameras

	## 🔍 Suggested Research Directions
	1. Cross-view consistency learning
	2. Novel view synthesis from sparse inputs
	3. Dynamic scene reconstruction
	4. Human motion transfer between viewpoints

	## 🚀 Access Information
	🎯 FULL Dataset Download Here: https://madacode.file.core.windows.net/root/360/MultiScene360%20Dataset.zip?sv=2023-01-03&st=2025-05-06T09%3A23%3A15Z&se=2028-01-07T09%3A23%3A00Z&sr=f&sp=r&sig=KnyHrAfeeCIpufeALFYRDAWDZ7W1F7hGUDToA26y9HQ%3D

	💼 Commercial Inquiries: contact@maadaa.ai
	Usage Rights:
	✔ Free for academic/commercial use
	✔ License: Attribution-NonCommercial-ShareAlike 4.0 International

	## About maadaa.ai
	Founded in 2015, maadaa.ai is a pioneering AI data service provider specializing in multimodal data solutions for generative AI development. We deliver end-to-end data services covering text, voice, image, and video datatypes – the core fuel for training and refining generative models.
	Our Generative AI Data Solution includes:
	ꔷ High-quality dataset collection & annotation tailored for LLMs and diffusion models
	ꔷ Scenario-based human feedback (RLHF/RLAIF) to enhance model alignment
	ꔷ One-stop data management through our MaidX platform for streamlined model training

	Why Choose Us:
	✓ Reduce real-world data collection costs by 70%+
	✓ Generate perfectly labeled training data at scale
	✓ API-first integration for synthetic pipelines

	Empowering the next generation of interactive media and spatial computing*

	---
	license: cc-by-sa-4.0
	---
	# recammaster_based_multi_camera_video_dataset
	A Real-World Multi-Camera Video Dataset for Generative Vision AI

	## 📌 Overview
	The MultiScene360 Dataset is designed to advance generative vision AI by providing synchronized multi-camera footage from real-world environments.

	💡 Key Applications:
	✔ Video generation & view synthesis
	✔ 3D reconstruction & neural rendering
	✔ Digital human animation systems
	✔ Virtual/augmented reality development

	## 📊 Dataset Specifications (Public Version)
	\| Category \| Specification \|
	\|---------------------\|----------------------------------------\|
	\| Scenes \| 10 base + 3 extended scenes \|
	\| Scene Duration \| 10-20 seconds each \|
	\| Camera Views \| 4 synchronized angles per scene \|
	\| Total Video Clips \| ~144 \|
	\| Data Volume \| 20-30GB (1080p@30fps) \|

	Commercial version available with 200+ scenes and 6-8 camera angles

	## 🌆 Complete Scene Specification
	\| ID \| Environment \| Location \| Primary Action \| Special Features \|
	\|------\|-------------\|-----------------\|----------------------------\|---------------------------\|
	\| S001 \| Indoor \| Living Room \| Walk → Sit \| Occlusion handling \|
	\| S002 \| Indoor \| Kitchen \| Pour water + Open cabinet \| Fine hand motions \|
	\| S003 \| Indoor \| Corridor \| Walk → Turn \| Depth perception \|
	\| S004 \| Indoor \| Desk \| Type → Head turn \| Upper body motions \|
	\| S005 \| Outdoor \| Park \| Walk → Sit (bench) \| Natural lighting \|
	\| S006 \| Outdoor \| Street \| Walk → Stop → Phone check \| Gait variation \|
	\| S007 \| Outdoor \| Staircase \| Ascend stairs \| Vertical movement \|
	\| S008 \| Indoor \| Corridor \| Two people passing \| Multi-person occlusion \|
	\| S009 \| Indoor \| Mirror \| Dressing + mirror view \| Reflection surfaces \|
	\| S010 \| Indoor \| Empty room \| Dance movements \| Full-body dynamics \|
	\| S011 \| Indoor \| Window \| Phone call + clothes adjust\| Silhouette + semi-reflections \|
	\| S012 \| Outdoor \| Shopping street \| Walking + window browsing \| Transparent surfaces + crowd \|
	\| S013 \| Indoor \| Night corridor \| Walking + light switching \| Low-light adaptation \|

	## 🎥 Camera Configuration
	Physical Setup:
	cam01──────cam02
	\ /
	Subject
	/ \
	cam04──────cam03

	Technical Details:
	- Cameras: DJI Osmo Action 5 Pro (4 identical units)
	- Mounting: Tripod-stabilized at ~1.5m height
	- Distance: 2-3m from subject center
	- FOV Overlap: 20-30% between adjacent cameras

	## 🔍 Suggested Research Directions
	1. Cross-view consistency learning
	2. Novel view synthesis from sparse inputs
	3. Dynamic scene reconstruction
	4. Human motion transfer between viewpoints

	## 🚀 Access Information
	🎯 FULL Dataset Download Here: https://madacode.file.core.windows.net/root/360/MultiScene360%20Dataset.zip?sv=2023-01-03&st=2025-05-06T09%3A23%3A15Z&se=2028-01-07T09%3A23%3A00Z&sr=f&sp=r&sig=KnyHrAfeeCIpufeALFYRDAWDZ7W1F7hGUDToA26y9HQ%3D

	💼 Commercial Inquiries: contact@maadaa.ai
	Usage Rights:
	✔ Free for academic/commercial use
	✔ License: Attribution-NonCommercial-ShareAlike 4.0 International

	## About maadaa.ai
	Founded in 2015, maadaa.ai is a pioneering AI data service provider specializing in multimodal data solutions for generative AI development. We deliver end-to-end data services covering text, voice, image, and video datatypes – the core fuel for training and refining generative models.
	Our Generative AI Data Solution includes:
	ꔷ High-quality dataset collection & annotation tailored for LLMs and diffusion models
	ꔷ Scenario-based human feedback (RLHF/RLAIF) to enhance model alignment
	ꔷ One-stop data management through our MaidX platform for streamlined model training

	Why Choose Us:
	✓ Reduce real-world data collection costs by 70%+
	✓ Generate perfectly labeled training data at scale
	✓ API-first integration for synthetic pipelines

	Empowering the next generation of interactive media and spatial computing*