From Single-Camera to Multi-Camera: Why Physical AI Needs Comprehensive Ego-Centric Data

With the advancement of VLA models, World Models, and humanoid robots, Physical AI is shifting its focus from “understanding the world” to “acting in the world.” Robots should not only perceive the environment but also understand how humans observe and interact with objects and perform complex tasks.

In this regard, Ego-Centric data has become a valuable resource for learning human behavior and interaction. However, what is evolving today is not Ego-Centric data itself, but rather the requirements of Physical AI models for it.

In the past, conventional first-person videos were enough to train a wide range of models. As robots perform increasingly complex and continuous manipulation tasks, models require data that captures the whole human operation process, including hand movements, object state changes, spatial relationships, and task trajectories, rather than just first-person perception.

Therefore, Ego-Centric data is evolving from conventional first-person video to a multi-camera data architecture.

From Single-Camera to Multi-Camera: Why Does Ego-Centric Data Evolve?

The evolution of Ego-Centric data follows the development of Physical AI models closely.

Typical Ego-Centric data collection projects use a single camera that records the first-person view of the operator. It naturally captures how humans perceive the environment, interact with objects, and perform tasks. At the same time, this solution is convenient and efficient to deploy at a large scale, making it suitable for collecting Ego-Centric data in bulk.

However, when robots start performing more complex long-horizon tasks like grasping, arranging, cleaning, and assembling, the limitations of the single-camera configuration become apparent. During fine-grained manipulation, hands and target objects may easily occlude each other, while using only one camera provides limited information for recovering depth, spatial relationships, and motion trajectories.

That is why multi-camera configurations are becoming increasingly attractive for collecting Ego-Centric data. With the ability to capture more visual perspectives, a dual-camera configuration provides richer depth and spatial cues, allowing models to better understand the 3D relationships between humans and objects while providing richer information for manipulation learning, trajectory recovery, and 3D reconstruction.

Now, Ego-Centric data collection is advancing even further toward a synchronized multi-camera collection approach.

Based on its existing experience in single-camera and dual-camera Ego-Centric data collection, Nexdata has started to develop multi-camera data collection methods. Besides conventional first-person cameras, wrist-mounted cameras and third-person cameras can be synchronized to capture the same task from multiple perspectives. First-person cameras record how the operator perceives the environment and executes the task; wrist-mounted cameras capture fine-grained manipulation information such as grasping, rotating, and placing objects; and third-person cameras record body posture and overall motion trajectories.

Such multi-perspective recording reduces information loss due to occlusion and provides more complete information about spatial relationships, motion, and task execution. It provides a richer data foundation for motion reconstruction, trajectory recovery, and complex manipulation behavior learning.

From single-camera to dual-camera and multi-camera collection, the evolution of Ego-Centric data is about more than just adding more first-person video. It is about synchronizing complementary views to fully reconstruct the interaction between humans and the physical environment.

Nexdata Expands Ego-Centric Capabilities With Multi-Camera Data Collection

As Physical AI models have increasing demands on the data used for training, Nexdata is expanding its Ego-Centric data production capabilities to include single-camera, dual-camera, and multi-camera collection to support different stages of robotics model development.

Depending on the needs of the project, Nexdata can configure synchronized combinations of first-person, third-person, and wrist-mounted cameras to record the operator’s perspective, fine-grained hand manipulation, body posture, object state changes, and full task execution from multiple views. The data collection hardware, camera configurations, task workflows, and data specifications can be customized according to model training needs.

Benefiting from Nexdata’s 8,000-square-meter Physical AI data factory, the team can quickly create household, industrial, warehouse, retail, and office environments for standardized, large-scale Ego-Centric data production. When data needs to be collected in a particular deployment environment, Nexdata can perform bespoke data collection in homes, factories, retail stores, and other application environments to produce data reflecting target operating conditions.

Besides data collection, Nexdata has created a portfolio of off-the-shelf Ego-Centric datasets with different camera configurations and application scenarios, allowing teams to speed up model training, algorithm validation, and data evaluation. In addition, Nexdata offers bespoke data collection, data annotation, and scenario design services, creating a flexible Ego-Centric data solution that combines ready-to-use datasets and customized data production to support continuous Physical AI model training and iteration.

Dataset List

10,000-Hour Egocentric Video Dataset for Robotics and AI Manipulation Training

1,042 Segments 6-camera Egocentric Embodied AI Dataset

Multi-Camera Data Changes the Value of Ego-Centric Data

The transition from single-camera to dual-camera and multi-camera collection is not just about adding more cameras. It reflects the increasing demands of Physical AI models for spatial information, fine-grained manipulation details, and task-level context.

Future robots should not only learn what humans see. They should also understand how humans interact with objects, how actions happen across multiple steps, and how objects and environments change during task execution. In this regard, multi-camera data capturing such processes from multiple perspectives will become increasingly important for learning complex robotic behaviors.

Nexdata will continue to expand its Ego-Centric data capabilities across single-camera, dual-camera, and multi-camera configurations. Combining off-the-shelf datasets with bespoke data production, Nexdata provides scalable and high-quality Ego-Centric data for robotics, VLA models, and World Models at different stages of Physical AI development.