TL;DR:
- Generative image models have gained popularity, but extending them to other modalities like audio, video, and 3D assets is a challenge.
- The paper introduces Shap•E, a conditional generative model using implicit neural representations (INRs) for 3D assets.
- INRs map 3D coordinates to location-specific information but have drawbacks like high computing costs and training difficulties.
- The researchers propose a transformer-based encoder to generate INR parameters for 3D assets.
- A conditional diffusion model is trained on the encoder’s outputs to produce INRs for neural radiance fields (NeRFs) and meshes.
- The encoder consumes point clouds and rendered views to output multi-layer perceptron (MLP) parameters.
- Shap•E outperforms a baseline model (Point•E) in terms of convergence speed and performance.
- Shap•E can generate diverse 3D objects without relying on intermediate image representations.
Main AI News:
Generative image models have captured the imagination of the general public, showcasing their remarkable text-to-image capabilities. However, the machine learning research community has been actively exploring avenues to expand these models beyond images, venturing into realms like audio, video, and 3D assets. Yet, the challenge lies in representing 3D assets efficiently and effectively for downstream applications.
In a groundbreaking paper titled “Shap•E: Generating Conditional 3D Implicit Functions,” a team of researchers from OpenAI introduces Shap•E, a conditional generative model that harnesses the power of implicit neural representations (INRs) to generate intricate and diverse 3D assets. Shap•E boasts accelerated convergence speed and achieves performance on par with existing methods while operating in higher-dimensional, multi-representation output space.
INRs have emerged as a flexible and expressive technique for representing 3D assets by mapping 3D coordinates to location-specific information such as color and density. However, there are drawbacks to this approach. Acquiring INRs for each sample in the dataset incurs a high computing cost, and the training of downstream generative models becomes more challenging due to the large number of numerical parameters associated with each INR.
This paper aims to advance the current state of INR approaches for diverse and complex 3D implicit representations. Building upon recent works by Chen & Wang and Dupont et al., the researchers adopt a transformer-based encoder, eschewing gradient-based meta-learning methods. This encoder is trained to generate INR parameters for 3D assets. Subsequently, a conditional diffusion model is trained on the encoder’s outputs to produce INRs that represent neural radiance fields (NeRFs) and meshes. This novel approach allows for multiple rendering techniques and seamless integration of INRs into various downstream 3D applications.
In the proposed method, the encoder takes point clouds and rendered views of a 3D asset as inputs, generating the parameters of a multi-layer perceptron (MLP) that represents the asset as an implicit function. Leveraging cross-attention mechanisms, the point cloud and input views are processed to yield latent representations in the form of a sequence of vectors. Each vector in this sequence undergoes transformation through a latent bottleneck and projection layer to produce the desired MLP parameters.
To validate the effectiveness of Shap•E, the research team conducted an empirical study comparing it to a baseline model called Point•E, an explicit generative model operating over point clouds. The experiments showcased Shap•E’s superiority, exhibiting faster convergence speeds and achieving comparable, if not superior, performance. Furthermore, Shap•E demonstrated its capability to generate diverse 3D objects without relying on intermediate image representations.
Conlcusion:
The introduction of Shap•E, a conditional generative model leveraging implicit neural representations (INRs) for 3D assets, represents a significant advancement in the market. With its ability to generate complex and diverse 3D objects without relying on intermediate image representations, Shap•E opens up new possibilities for various industries, including entertainment, gaming, virtual reality, and architecture. Its faster convergence speed and comparable or superior performance compared to existing models position Shap•E as a valuable tool for businesses seeking to create realistic and diverse 3D assets efficiently.
Moreover, the incorporation of INRs into downstream applications paves the way for enhanced rendering approaches and improved 3D asset utilization. As the demand for realistic and immersive experiences continues to grow, Shap•E’s innovative capabilities position it at the forefront of the market, offering businesses a competitive edge and unlocking new avenues for creative expression and visual storytelling.
