text2mesh VS neural-style-pt

Compare text2mesh vs neural-style-pt and see what are their differences.

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text2mesh neural-style-pt
14 35
907 817
0.4% -
2.1 0.0
10 months ago over 1 year ago
Jupyter Notebook Python
MIT License MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

text2mesh

Posts with mentions or reviews of text2mesh. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-12-27.

neural-style-pt

Posts with mentions or reviews of neural-style-pt. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-12-10.

What are some alternatives?

When comparing text2mesh and neural-style-pt you can also consider the following projects:

SAFA - Official Pytorch Implementation of 3DV2021 paper: SAFA: Structure Aware Face Animation.

STROTSS - Style Transfer by Relaxed Optimal Transport and Self-Similarity (CVPR 2019)

Clip-Forge

StyleGAN-nada

CenterSnap - Pytorch code for ICRA'22 paper: "Single-Shot Multi-Object 3D Shape Reconstruction and Categorical 6D Pose and Size Estimation"

style-transfer-pytorch - Neural style transfer in PyTorch.

pytorch-CycleGAN-and-pix2pix - Image-to-Image Translation in PyTorch

artistic-videos - Torch implementation for the paper "Artistic style transfer for videos"

NeROIC

ada-conv-pytorch

magic3d-pytorch - Implementation of Magic3D, Text to 3D content synthesis, in Pytorch

Styleformer - A Neural Language Style Transfer framework to transfer natural language text smoothly between fine-grained language styles like formal/casual, active/passive, and many more. Created by Prithiviraj Damodaran. Open to pull requests and other forms of collaboration.