neural-deferred-shading VS GAN2Shape

Compare neural-deferred-shading vs GAN2Shape and see what are their differences.

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neural-deferred-shading GAN2Shape
1 1
239 570
5.4% -
4.6 0.0
2 months ago 10 months ago
Python Python
GNU General Public License v3.0 or later 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.

neural-deferred-shading

Posts with mentions or reviews of neural-deferred-shading. We have used some of these posts to build our list of alternatives and similar projects.

GAN2Shape

Posts with mentions or reviews of GAN2Shape. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing neural-deferred-shading and GAN2Shape you can also consider the following projects:

ECON - [CVPR'23, Highlight] ECON: Explicit Clothed humans Optimized via Normal integration

anycost-gan - [CVPR 2021] Anycost GANs for Interactive Image Synthesis and Editing

surface_normal_uncertainty - (ICCV 2021 - oral) Estimating and Exploiting the Aleatoric Uncertainty in Surface Normal Estimation

ALAE - [CVPR2020] Adversarial Latent Autoencoders

MICA - MICA - Towards Metrical Reconstruction of Human Faces [ECCV2022]

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

3d-transforms - 3D Transforms is a library to easily work with 3D data and make 3D transformations. This library originally started as a few functions here and there for my own work which I then turned into a library.

2dimageto3dmodel - We evaluate our method on different datasets (including ShapeNet, CUB-200-2011, and Pascal3D+) and achieve state-of-the-art results, outperforming all the other supervised and unsupervised methods and 3D representations, all in terms of performance, accuracy, and training time.

BlenderNeRF - Easy NeRF synthetic dataset creation within Blender