10 Elements of Employee Engagement

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When employees communicate well, are excited to go to work, collaborate with each other and have healthy team dynamics, chances are that they are highly engaged. Employee engagement affects these and more aspects of the employee experience. It is the level to which employees feel mentally and emotionally connected to their jobs.

Employee engagement will also affect customer relations and profitability of businesses. To maintain it, there are aspects of employee engagement every organisation should consider.

Before deploying the elements of employee engagement, it is necessary to measure where your employee engagement stands.

How to measure employee engagement

This will reveal what exactly needs to be improved. Surveys and one-on-one conversations are some of the tactics that can be used to measure employee engagement. However, you need to plan a measuring strategy that covers some of the following areas:

Identifying what is important to your employees

Use surveys that seek to find out what is important to your employees. This way you will be able to create engagement drivers that will actually have an impact.

It should be continuous

Employee engagement cannot be measured once. Rather it is an area that needs to be revisited time and again. Your measuring strategy should have a schedule for how often you will get employee feedback.

Survey all your staff

It might seem effective to only survey a sample of staff but this will not give a true picture of how engagement stands. Design an engaging survey and administer it to all staff.

Tailor the measurement to specific groups

Once you have measured all the staff, you may receive data that indicates that a certain group or team has a different experience. Delve deeper by asking these different groups more specific questions.

Once you have the results, employees can be classified as highly engaged, moderately engaged, barely engaged and disengaged.

Highly engaged: These hold the workplace and the work they do in high regard. They go out of their way and put extra effort to help the company succeed. They also speak positively about the company outside of work.

Moderately engaged: These employees view the company positively but they see areas for improvement. They are unlikely to go the extra mile or ask for more responsibility.

Barely engaged: This group exhibits low motivation and will only do what they need to do. They do not dislike the workplace, but neither do they like it.

Disengaged: This group has a negative view of the workplace and may tell others about this. They do not feel an affinity with the values or goals at work. Their attitude can have a negative impact on those around them.

Once you have the results, set about improving or maintaining engagement if it is high.

10 elements of employee engagement


Trust is essential if you wish to have a highly engaged workforce. This includes a number of things: bosses trusting employees to take on new tasks, management sharing information with employees about how the company is performing and employees trusting that they can share failures with their supervisors.

It also extends to trust between colleagues and team mates. It makes for an engaged staff when employees feel like they don’t need to walk around on eggshells in the workplace.


Workplaces should strive to create and maintain a positive workplace culture. Culture constitutes the behaviours and customs of the workplace. Some aspects of culture may be intangible but if it is positive, it will result in employees who are happy to come to work. In addition to improved engagement, a positive culture can improve productivity and profitability.

Professional growth

Yet another element of employee engagement is professional growth. Have an employee development program to ensure that employees are continuously improving their skills. While there might be specific areas required for all employees, ask specific employees what areas they are interested in and skill them in these as well.

Ward off employee stagnation through conferences, work retreats and online courses. Mentorship and shadowing of senior colleagues is another means of professional development.


Nothing can be as demotivating as continuously doing good work and receiving no recognition. According to one employee survey report, employees wanted their boss to give them more recognition more than they wanted frequent check ins (8%) and career growth (19%)

Pay attention to high achievers and reward them. An employee recognition and rewards program can make this even easier. Don’t forget to include informal ways for showing recognition. This will encourage a culture of gratitude amongst staff. Plus, recognition shouldn’t only be reserved for employees who hit targets. Recognise employees who served customers well and who helped colleagues out.


Good communication in the workplace is a big driver for engagement. Without it, tasks may go undone or get done wrongly. It is prudent to set up clear lines of communication in the workplace.

Some parts of communication lie in culture. What is the relationship between managers and their staff? Do employees interact outside of work? If the workplace supports such, they will help positive communication.

There should be open communication between upper management and the staff. Letting staff know how the business is performing will make them feel like part of the company as opposed to just workers.


Good leaders foster retention, improve morale and positively affect culture. Offer comprehensive periodic leadership training right from senior management to team leaders. These training sessions should include knowledge on soft skills like listening, creativity, problem solving, empathy and emotional intelligence.

Clear vision

Engaged employees understand the big picture and how they fit into it. A clearly communicated vision and an understanding of core values give employees something to rally around. If employees feel like a part of something bigger than themselves, they are much more likely to go above and beyond to contribute to that greater purpose.

Challenging work

For employees to be engaged at work their tasks should be challenging but doable. Too easy work doesn’t give a sense of satisfaction upon completion.

If employees are doing their work right, they should master their tasks in time. This is where the need for professional growth and advancement come in. Learning new things and taking on new challenging tasks every so often will boost employee engagement.

Meaningful work

Employees need to be able to answer questions such as “Why am i doing this job?” and “What is the point of this?” While meaning to work can be deeply individual and vary from one employee to the next, management can help by articulating the benefits of one’s work to other stakeholders.

Corporate Social Responsibility (CSR)

Closely related to having meaningful work is CSR. CSR drives engagement because employees know that their efforts are impacting someone else. This works even better if employees have a hand in selecting the charities or activities that the organisation supports.

In Summary

Employee engagement is always going to be important to businesses. This is because it affects all aspects of an organisation, both internal and external. Luckily, there are numerous elements of employee engagement that you can deploy.

It is however important to do this after understanding where engagement rates lie and getting an understanding of which specific elements will work for your employees.

The post 10 Elements of Employee Engagement appeared first on The 6Q Blog.

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DynIBaR: Space-time view synthesis from videos of dynamic scenes

Posted by Zhengqi Li and Noah Snavely, Research Scientists, Google Research

A mobile phone’s camera is a powerful tool for capturing everyday moments. However, capturing a dynamic scene using a single camera is fundamentally limited. For instance, if we wanted to adjust the camera motion or timing of a recorded video (e.g., to freeze time while sweeping the camera around to highlight a dramatic moment), we would typically need an expensive Hollywood setup with a synchronized camera rig. Would it be possible to achieve similar effects solely from a video captured using a mobile phone’s camera, without a Hollywood budget?

In “DynIBaR: Neural Dynamic Image-Based Rendering”, a best paper honorable mention at CVPR 2023, we describe a new method that generates photorealistic free-viewpoint renderings from a single video of a complex, dynamic scene. Neural Dynamic Image-Based Rendering (DynIBaR) can be used to generate a range of video effects, such as “bullet time” effects (where time is paused and the camera is moved at a normal speed around a scene), video stabilization, depth of field, and slow motion, from a single video taken with a phone’s camera. We demonstrate that DynIBaR significantly advances video rendering of complex moving scenes, opening the door to new kinds of video editing applications. We have also released the code on the DynIBaR project page, so you can try it out yourself.

Given an in-the-wild video of a complex, dynamic scene, DynIBaR can freeze time while allowing the camera to continue to move freely through the scene.


The last few years have seen tremendous progress in computer vision techniques that use neural radiance fields (NeRFs) to reconstruct and render static (non-moving) 3D scenes. However, most of the videos people capture with their mobile devices depict moving objects, such as people, pets, and cars. These moving scenes lead to a much more challenging 4D (3D + time) scene reconstruction problem that cannot be solved using standard view synthesis methods.

Standard view synthesis methods output blurry, inaccurate renderings when applied to videos of dynamic scenes.

Other recent methods tackle view synthesis for dynamic scenes using space-time neural radiance fields (i.e., Dynamic NeRFs), but such approaches still exhibit inherent limitations that prevent their application to casually captured, in-the-wild videos. In particular, they struggle to render high-quality novel views from videos featuring long time duration, uncontrolled camera paths and complex object motion.

The key pitfall is that they store a complicated, moving scene in a single data structure. In particular, they encode scenes in the weights of a multilayer perceptron (MLP) neural network. MLPs can approximate any function — in this case, a function that maps a 4D space-time point (x, y, z, t) to an RGB color and density that we can use in rendering images of a scene. However, the capacity of this MLP (defined by the number of parameters in its neural network) must increase according to the video length and scene complexity, and thus, training such models on in-the-wild videos can be computationally intractable. As a result, we get blurry, inaccurate renderings like those produced by DVS and NSFF (shown below). DynIBaR avoids creating such large scene models by adopting a different rendering paradigm.

DynIBaR (bottom row) significantly improves rendering quality compared to prior dynamic view synthesis methods (top row) for videos of complex dynamic scenes. Prior methods produce blurry renderings because they need to store the entire moving scene in an MLP data structure.

Image-based rendering (IBR)

A key insight behind DynIBaR is that we don’t actually need to store all of the scene contents in a video in a giant MLP. Instead, we directly use pixel data from nearby input video frames to render new views. DynIBaR builds on an image-based rendering (IBR) method called IBRNet that was designed for view synthesis for static scenes. IBR methods recognize that a new target view of a scene should be very similar to nearby source images, and therefore synthesize the target by dynamically selecting and warping pixels from the nearby source frames, rather than reconstructing the whole scene in advance. IBRNet, in particular, learns to blend nearby images together to recreate new views of a scene within a volumetric rendering framework.

DynIBaR: Extending IBR to complex, dynamic videos

To extend IBR to dynamic scenes, we need to take scene motion into account during rendering. Therefore, as part of reconstructing an input video, we solve for the motion of every 3D point, where we represent scene motion using a motion trajectory field encoded by an MLP. Unlike prior dynamic NeRF methods that store the entire scene appearance and geometry in an MLP, we only store motion, a signal that is more smooth and sparse, and use the input video frames to determine everything else needed to render new views.

We optimize DynIBaR for a given video by taking each input video frame, rendering rays to form a 2D image using volume rendering (as in NeRF), and comparing that rendered image to the input frame. That is, our optimized representation should be able to perfectly reconstruct the input video.

We illustrate how DynIBaR renders images of dynamic scenes. For simplicity, we show a 2D world, as seen from above. (a) A set of input source views (triangular camera frusta) observe a cube moving through the scene (animated square). Each camera is labeled with its timestamp (t-2, t-1, etc). (b) To render a view from camera at time t, DynIBaR shoots a virtual ray through each pixel (blue line), and computes colors and opacities for sample points along that ray. To compute those properties, DyniBaR projects those samples into other views via multi-view geometry, but first, we must compensate for the estimated motion of each point (dashed red line). (c) Using this estimated motion, DynIBaR moves each point in 3D to the relevant time before projecting it into the corresponding source camera, to sample colors for use in rendering. DynIBaR optimizes the motion of each scene point as part of learning how to synthesize new views of the scene.

However, reconstructing and deriving new views for a complex, moving scene is a highly ill-posed problem, since there are many solutions that can explain the input video — for instance, it might create disconnected 3D representations for each time step. Therefore, optimizing DynIBaR to reconstruct the input video alone is insufficient. To obtain high-quality results, we also introduce several other techniques, including a method called cross-time rendering. Cross-time rendering refers to the use of the state of our 4D representation at one time instant to render images from a different time instant, which encourages the 4D representation to be coherent over time. To further improve rendering fidelity, we automatically factorize the scene into two components, a static one and a dynamic one, modeled by time-invariant and time-varying scene representations respectively.

Creating video effects

DynIBaR enables various video effects. We show several examples below.

Video stabilization

We use a shaky, handheld input video to compare DynIBaR’s video stabilization performance to existing 2D video stabilization and dynamic NeRF methods, including FuSta, DIFRINT, HyperNeRF, and NSFF. We demonstrate that DynIBaR produces smoother outputs with higher rendering fidelity and fewer artifacts (e.g., flickering or blurry results). In particular, FuSta yields residual camera shake, DIFRINT produces flicker around object boundaries, and HyperNeRF and NSFF produce blurry results.

Simultaneous view synthesis and slow motion

DynIBaR can perform view synthesis in both space and time simultaneously, producing smooth 3D cinematic effects. Below, we demonstrate that DynIBaR can take video inputs and produce smooth 5X slow-motion videos rendered using novel camera paths.

Video bokeh

DynIBaR can also generate high-quality video bokeh by synthesizing videos with dynamically changing depth of field. Given an all-in-focus input video, DynIBar can generate high-quality output videos with varying out-of-focus regions that call attention to moving (e.g., the running person and dog) and static content (e.g., trees and buildings) in the scene.


DynIBaR is a leap forward in our ability to render complex moving scenes from new camera paths. While it currently involves per-video optimization, we envision faster versions that can be deployed on in-the-wild videos to enable new kinds of effects for consumer video editing using mobile devices.


DynIBaR is the result of a collaboration between researchers at Google Research and Cornell University. The key contributors to the work presented in this post include Zhengqi Li, Qianqian Wang, Forrester Cole, Richard Tucker, and Noah Snavely.

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