The dataset can often limit AI researchers by cripling the results of their training model. That can have a crucial impact on the outcome of the research and usually does not reveal the full potential of the researcher’s work. When building a good dataset, the best way is always to use data from the world surrounding us. However, sometimes this can be hard and labour-intensive and processing all that data can be nearly impossible. A great way to create a good dataset for AI is to create 3D models of the objects. Photorealistic objects can be 95-99%+ close to real life and offer an excellent base for training models. However, not every subject is easy to be created as a 3D model. For example, one of the most complex subjects is creating 3d Models of Humans. One way is to hire a high-skilled artist that can make a photorealistic 3D model that can fool the human eye. However, this approach will take months of work for just one model and will cost quite a lot. That might be okay for one model, but what about a hundred or thousands?
That’s why we have developed a better approach by 3D Scanning real humans and creating 3D models that can be used as a dataset in a wide range of AI Researches.
That approach allows us to develop large-scale photorealistic 3D human models with great diversity. This approach gets all of the benefits of photorealistic 3D models while cutting costs and production three to five times. But that does not solve all issues.
Having high-quality data requires more than just creating 3D models.
The first problem with 3D Scanning is a large number of people with a wide diversity in age, ethnicity, BMI, etc. We recently worked with a company to create an extensive database of scanned 3D models with wide racial and age diversity, and we approached that problem using our casting service.
Working with local talent and international casting agencies, we created a casting selection for models that fit the age and ethnic criteria. Once our client approved that selection, we proceeded with Full Body 3D scanning of each model.
We used our sophisticated full-body 3d scanning rig that created very High-Resolution Human 3D Scans, which allowed our client to fulfil his database for their training models and develop researchers even faster.
The second problem with gathering an extensive human database is how to prepare that 3D human database into a valuable dataset.
The raw 3d scans have great textures and surface detail; however, they are also cumbersome for large-scale computation. Luckily there is a way to preserve the quality while optimizing the topology and textures. We have developed a workflow that automates most mundane tasks while keeping the human touch where needed to ensure the quality of each scan is up to industry standards and our clients.
Identifying and classifying human joints, called Human Pose Estimation (HPE), is a fundamental problem in machine learning, which deals with defining a set of coordinates for each joint and can describe the pose of a human. The most common approaches to the model of the human body are Skeleton-based, Contour-based model and Volume-based model.No matter which one is used, having a dataset of 3d scanned human poses can quickly generate accurate training data, which saves precious time for researchers to focus on adjusting their training model.
Facial recognition is another classic problem that challenges machine learning. While humans recognize faces without much effort, a machine-learning algorithm requires extensive training to perform as well as humans or even better. An even more challenging task is to recognize facial expressions. Traditional techniques for FR use facial markers for the eyes, mouth, nose, eyebrows and more. Based on that, ML agents can learn to recognize faces with very high accuracy. However, we rely on an accurate dataset of faces and expressions with markers to get good results. This is again a great example of 3d scanned faces being able to help researchers to a great extent to prepare training data.
Any information manufactured artificially and does not represent events or objects in the real world is classified as Synthetic Data.
The generation of Synthetic has substantial benefits when training machine learning (ML) models. They range from low cost, scalability and ease of use to helping data scientists comply with privacy regulations such as HIPAA, GDPR, CCPA, and CPA.
Synthetic data can help research scientists expand the training parameters of their ML model by presenting training scenarios that are rare or expensive to capture while keeping the cost low.
Recently we worked on a project where we helped with, which included a generation of realistic human faces with multiple variances in hair, eyes, lipstick and more.