This is the tf.keras implementation of the volumetric variational autoencoder (VAE) described in the paper "Generative and Discriminative Voxel Modeling with Convolutional Neural Networks".

Dec 23, 2024 · Recent 3D content generation pipelines commonly employ Variational Autoencoders (VAEs) to encode shapes into compact latent representations for diffusion …

一个比较直观的思路，就是将每一帧生成过程与过去的帧相关联，而非直接取关键帧来进行压缩。在最近的几个高质量的开源视频生成算法中，都将2D-VAE+关键帧，转变为以CausalConv3d …

We present Dora-VAE for high-quality 3D reconstruction, and Dora-Bench for 3D VAE evaluation. The improved reconstruction quality offered by Dora-VAE can directly boost the performance …

Variational autoencoders (VAEs) play an important role in high-dimensional data generation based on their ability to fuse the stochastic data representation with the power of recent deep …

Nov 24, 2021 · In this paper, we propose an intuitive yet effective self-supervised approach to train a 3D shape variational autoencoder (VAE) which encourages a disentangled latent …

This is an implementation of the VQ-VAE (Vector Quantized Variational Autoencoder) and Convolutional Varational Autoencoder. from Neural Discrete representation learning for …

Geometry VAE. The key to generating high-quality 3D scenes with a latent diffusion model is our Geometry VAE, capable of compressing pointmaps with high accuracy. We find empirically …

Sep 13, 2017 · We propose a novel framework which we call mesh variational autoencoders (mesh VAE), to explore the probabilistic latent space of 3D surfaces. The framework is easy to …

In this paper, we propose an intuitive yet effective self-supervised approach to train a 3D shape variational autoencoder (VAE) which encourages a disentangled latent representa-tion of …