Methods and systems for converting a line drawing to a rendered image
The system includes a memory that stores instructions for executing processes converting line drawings to rendered images. The system also includes a processor configured to execute the instructions. The instructions cause the processor to: train a neural network to account for irregularities in the line drawings by introducing noise data into training data of the neural network; receive a first line drawing from an input device; generate a first rendered image based on features identified in the first line drawing; and display the first rendered image on an output device.
1. A system comprising:
a memory that stores instructions for executing processes for converting line drawings to rendered images; and
a processor configured to execute the instructions, wherein the instructions cause the processor to:
train a neural network to account for irregularities in the line drawings by introducing noise data into training data of the neural network;
receive a first line drawing from an input device;
generate a first rendered image based on features identified in the first line drawing; and
display the first rendered image on an output device.
2. The system of claim 1 , wherein the neural network is a conditional Generative Adversarial Network (cGAN).
3. The system of claim 1 , wherein the noise data comprises different types of edge noise.
4. The system of claim 3 , wherein the different types of edge noise comprises high-frequency noise, low-frequency noise, and repetitive noise.
5. The system of claim 1 , wherein the processor is further configured to train the neural network using a database of images, and wherein the first line drawing is a hand-drawn image.
6. The system of claim 5 , wherein generating the first rendered image comprises compiling one or more digital images that match respective features identified in the first line drawing.
7. The system of claim 5 , wherein the hand-drawn image is a partial image, and generating the rendered image comprises inserting omitted elements of the partial image based on relationships learned from the database of images.
8. A method comprising:
training a neural network to account for irregularities in line drawings by introducing noise data into training data of the neural network;
receiving a first line drawing from an input device;
generating a first rendered image based on features identified in the first line drawing; and
displaying the first rendered image on an output device.
9. The method of claim 8 , wherein the neural network is a conditional Generative Adversarial Network (cGAN).
10. The method of claim 8 , wherein the noise data comprises different types of edge noise.
11. The method of claim 10 , wherein the different types of edge noise comprises high-frequency noise, low-frequency noise, and repetitive noise.
12. The method of claim 8 , further comprising training the neural network using a database of images, and wherein the first line drawing is a hand-drawn image.
13. The method of claim 12 , wherein generating the first rendered image comprises compiling one or more digital images that match respective features identified in the first line drawing.
14. The method of claim 12 , wherein the hand-drawn image is a partial image, and generating the rendered image comprises inserting omitted elements of the partial image based on relationships learned from the database of images.
15. A non-transitory computer-readable storage medium containing executable computer program code, the code comprising instructions configured to cause a processor to:
train a neural network to account for irregularities in line drawings by introducing noise data into training data of the neural network;
receive a first line drawing from an input device;
generate a first rendered image based on features identified in the first line drawing; and
display the first rendered image on an output device.
16. The medium of claim 15 , wherein the neural network is a conditional Generative Adversarial Network (cGAN).
17. The medium of claim 15 , wherein the noise data comprises different types of edge noise including high-frequency noise, low-frequency noise, and repetitive noise.
18. The medium of claim 15 , wherein the processor is further configured to train the neural network using a database of images, and wherein the first line drawing is a hand-drawn image.
19. The medium of claim 18 , wherein generating the first rendered image comprises compiling one or more digital images that match respective features identified in the first line drawing.
20. The medium of claim 18 , wherein the hand-drawn image is a partial image, and generating the rendered image comprises inserting omitted elements of the partial image based on relationships learned from the database of images.