We are witnessing an onset of AI driven technologies, that are baffling all of us. Be it the Chat GPT, Jasper or the Dall E, artificial intelligence is slowly showing us its true power. The latest entrant to these technologies are the 3D model generators, especially the Point E by Open AI. It is mesmerizing for all of us.
What Actually Is Point E?
Point E is a 3D model generator released recently by an AI company named Open AI. The name Point E is comprised if two words, which are Point and E. The “Point” signifies that the 3D model is made using point clouds, and the “E” denotes the efficiency, as this generator is very efficient than its predecessors.
Point E is available in two models, one text to image model, and the other one is image to 3D model. To generate a 3D model of an object, one has to use textual prompts to get an image, which is then given to “image to 3D” model. This model then creates a point cloud based 3D model, based upon its training.
While describing the functioning of Point E, its developer team noted, “Our method first generates a single synthetic view using a text-to-image diffusion model and then produces a 3D point cloud using a second diffusion model which conditions on the generated image”.
The best thing about Point E in comparison to other AI generators, is that it does not use much of computational and energy resources. It uses just a single GPU and takes just a few seconds to convert text into a 3D model.
Limitations Of Point E AI Tool
Even though Point E is a very powerful and efficient tool to generate 3D models, but it is having many limitations. Some of them are discussed below;
1. Unfinished Models
This is the first and the very visible limitation of Point E. The models generated with Point E lack the original fine-grained shape and texture, and it is because of the point clouds.
Point E uses point clouds for model generation because they are very efficient from computational point of view, but they take away the model’s shape and texture.
To overcome this limitation, the company has started making use of meshes, but it is still not free of flaws.
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2. Failure To Develop The Desired Model
Point E sometimes develops such models that do not match the text prompts. It is because the “image to 3D model” sometimes fails to understand the image developed by the “text to image model”.
In some cases, it has been seen that the final model sometimes lacks some parts, and therefore looks very distorted in nature.
3. Inherent Bias
Since Point E is an AI driven model generators that has been trained on a vast amounts of data. Any kind of bias present in that data will definitely appear in the actions of Point E. In addition, any bias present in its trainers will also affect its performance.
4. Copyright issues
This is yet another limitation of Point E. If it rolls on, then it might give rise to intellectual property infringement issues.
The model that Point E generates will definitely be influenced by the works of previous artists, and hence they make consider it to be violating their Intellectual property rights.
Even though Point E has many limitations, but the way it has cut down the requirement of computational resources, energy resources and time, shows that it can be the next big industry disruptor.
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So, this was all about the Open AI’s 3D model generator Point E, and its limitations. If you loved this article, kindly share it with your friends.
Point E is a 3D model generator released recently by Open AI, the same company that owns Chat GPT. It uses text prompts to generate 3D models in a fraction of a minute. It has been named as Point E because it uses Point cloud to make a model and it is very Efficient than its other counterparts.
Point E is owned by Open AI and it is open sourced right now. It allows users to use textual inputs to develop 3D models. It is seen as a very effective counterpart to Dall E and other 3D model generators.
No, Point E is not flawless at all. Even though it generates 3D models very quickly and without using many computational resources, but it still has some flaws. The images generated by it are not sharp and lack the original texture. In addition, sometimes it fails to produce the desired models, apart from having the IPR issues and bias issues.