Introduction
Have you ever dreamed of creating your own 3D shapes that look like they came out of a movie or a video game? a new model that can make amazing 3D shapes from a hidden code. It was invented by a team of smart researchers from different places around the world, such as Computer Vision Lab - ETH Zurich, Snap Inc, CI2CV Lab - CMU, ESAT - KULeuven. They used a clever technique called diffusion models, which can learn how to make complex shapes by adding and removing noise. They wanted to make a better model than the ones that already exist, which have problems like low quality, emptiness, or unevenness. This new model can make high-quality shapes that look natural and realistic, and you can also change them however you want in the hidden code. They call this new model as '3D VADER'.
What is 3D VADER?
The 3D VADER model uses a novel approach to training diffusion models. It trains the model to predict the next step in a diffusion process, which gradually transforms a shape from a simple starting point to its final form. The model also uses an autoregressive decoder, which allows for more efficient generation of shapes.
3D VADER is an AutoDecoding Latent 3D Diffusion Model that generates high-quality 3D shapes by using a novel approach to training diffusion models. The model is trained to generate shapes by predicting the next step in a diffusion process, which gradually transforms a shape from a simple starting point to its final form.
Key Features of 3D VADER
Some of the key features of the 3D VADER model include:
3D VADER can generate realistic and diverse 3D shapes from a latent space that is easy to manipulate and interpolate.
- It can handle complex shapes with fine details and sharp features, such as cars, airplanes, and chairs.
- It can generate shapes that are consistent with natural priors, such as symmetry, smoothness, and connectivity.
- The model uses a novel approach to training diffusion models that improves their performance.
- The use of an autoregressive decoder allows for more efficient generation of shapes.
Capabilities/Use Case of 3D VADER
Some of the capabilities and use cases of 3D VADER are:
- It can be used for 3D shape synthesis, where one can generate new shapes by sampling from the latent space or interpolating between existing shapes.
- It can be used for 3D shape editing, where one can modify existing shapes by manipulating their latent vectors or applying transformations in the latent space.
- It can be used for 3D shape completion, where one can infer missing parts of incomplete shapes by encoding them into the latent space and decoding them back into the voxel space.
- It can be used for 3D shape retrieval, where one can find similar shapes in a large database by comparing their latent vectors or using nearest neighbor search.
- It can be used for 3D shape analysis, where one can extract semantic features or attributes from shapes by using their latent vectors or applying clustering or classification techniques.
How to access and use this model?
The 3D VADER is available on GitHub, where one can find the source code. The model can be used locally on a machine with dependencies installed.
The model is open source, but its license is not specified in the repository. Therefore, it is not clear whether it can be used for commercial purposes. I recommend checking the GitHub repository and contacting the authors for more information on the licensing of the model.
If you are interested to learn more about this model, all relevant links are provided under the 'source' section at the end of this article.
How does 3D VADER work?
3D VADER is a new way of making 3D assets using a special decoder that can turn a hidden code into a 3D volume that you can see and render. It has two steps (as shown in above figure): First, it learns a secret code for each object in your dataset, and how to turn it into a smaller 3D volume. Then, it learns how to make the smaller volume bigger and brighter, so you can render it from any angle. Second, it freezes the decoder and trains a noise remover that can clean up any random code and make it look like a real object. When you want to make a new shape, you just pick a random code, clean it up, and decode it. You have a new 3D shape.
You don’t even need to know where the camera is or how to move it. 3D VADER can figure that out by itself or use the camera information you already have. It’s that smart and flexible.
Performance evaluation with other Models
The performance of the 3D VADER model has been evaluated against other state-of-the-art models and has been shown to outperform them in terms of the quality of the generated shapes. According to the research paper, 3D VADER achieves state-of-the-art results on various benchmark datasets and metrics, including multi-view image datasets of synthetic objects, real in-the-wild videos of moving people, and a large-scale, real video dataset of static objects.
In particular, 3D VADER’s performance on the ShapeNet dataset, where it outperforms existing methods in terms of FID and LPIPS scores. Performance Evaluation also presents qualitative results that demonstrate the high quality and diversity of the shapes generated by 3D VADER.
source - https://arxiv.org/pdf/2307.05445.pdf
Overall, the performance evaluation presented in the research paper suggests that 3D VADER is a powerful and effective model for generating high-quality 3D shapes.
Limitations and Future Work
While 3D VADER has demonstrated impressive and state-of-the-art results on diverse tasks and content, there are still several challenges and limitations that remain.
- The model can only handle images and videos with one key person or object in the foreground, not complex scenes with multiple objects.
- The model needs multi-view or video sequences of each object for training, not single images. This limits the size and diversity of the datasets that can be used.
A possible future direction is to use general knowledge from existing datasets to learn new object categories from single images. This would enable the generation of more content with less data.
Conclusion
In conclusion, the 3D VADER model represents a significant advancement in the generation of high-quality 3D shapes using diffusion models. As this technology continues to develop, it has the potential to have a significant impact on many different fields.
source
research paper - https://arxiv.org/abs/2307.05445
research document - https://arxiv.org/pdf/2307.05445.pdf
Github repo - https://github.com/snap-research/3DVADER
Project details - https://snap-research.github.io/3DVADER/
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