# Enhanced Mixture 3D CGAN for Completion and Generation of 3D Objects

arXiv: https://arxiv.org/abs/2602.08046 · HTML: https://mlsanity.com/paper/2602.08046/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Vision mixture-of-experts, one of 12 directions in Mixture-of-experts.
- Builds on 17 of its 32 references that are in the map.
- First posted on arXiv 8 Feb 2026.

## Placed in

- Mixture-of-experts / [Vision mixture-of-experts](https://mlsanity.com/moe/vision-mixture-of-experts/)

## Builds on (most cited first, 17 of 17)

- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [ShapeNet: An Information-Rich 3D Model Repository](https://mlsanity.com/paper/1512.03012/) (2015)
- [Extracting Training Data from Diffusion Models](https://mlsanity.com/paper/2301.13188/) (2023)
- [Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling](https://mlsanity.com/paper/1610.07584/) (2016)
- [The relativistic discriminator: a key element missing from standard GAN](https://mlsanity.com/paper/1807.00734/) (2018)
- [PF-Net: Point Fractal Network for 3D Point Cloud Completion](https://mlsanity.com/paper/2003.00410/) (2020)
- [Unsupervised 3D Shape Completion through GAN Inversion](https://mlsanity.com/paper/2104.13366/) (2021)
- [Unpaired Point Cloud Completion on Real Scans using Adversarial Training](https://mlsanity.com/paper/1904.00069/) (2019)
- [Adversarial Generation of Continuous Implicit Shape Representations](https://mlsanity.com/paper/2002.00349/) (2020)
- [Reconstruction of three-dimensional porous media using generative adversarial neural networks](https://mlsanity.com/paper/1704.03225/) (2017)
- [DiffComplete: Diffusion-based Generative 3D Shape Completion](https://mlsanity.com/paper/2306.16329/) (2023)
- [MEGAN: Mixture of Experts of Generative Adversarial Networks for Multimodal Image Generation](https://mlsanity.com/paper/1805.02481/) (2018)
- [Improved Training of Mixture-of-Experts Language GANs](https://mlsanity.com/paper/2302.11875/) (2023)
- [Hierarchical Mixtures of Generators for Adversarial Learning](https://mlsanity.com/paper/1911.02069/) (2019)
- [VAEs and GANs: Implicitly Approximating Complex Distributions with Simple Base Distributions and Deep Neural Networks -- Principles, Necessity, and Limitations](https://mlsanity.com/paper/2503.01898/) (2025)
- [Inferring 3D Shapes from Image Collections using Adversarial Networks](https://mlsanity.com/paper/1906.04910/) (2019)

## Abstract (arXiv)

The generation and completion of 3D objects represent a transformative challenge in computer vision. Generative Adversarial Networks (GANs) have recently demonstrated strong potential in synthesizing realistic visual data. However, they often struggle to capture complex and diverse data distributions, particularly in scenarios involving incomplete inputs or significant missing regions. These challenges arise mainly from the high computational requirements and the difficulty of modeling heterogeneous and structurally intricate data, which restrict their applicability in real-world settings. Mixture of Experts (MoE) models have emerged as a promising solution to these limitations. By dynamically selecting and activating the most relevant expert sub-networks for a given input, MoEs improve both performance and efficiency. In this paper, we investigate the integration of Deep 3D Convolutional GANs (CGANs) with a MoE framework to generate high-quality 3D models and reconstruct incomplete or damaged objects. The proposed architecture incorporates multiple generators, each specialized to capture distinct modalities within the dataset. Furthermore, an auxiliary loss-free dynamic capacity constraint (DCC) mechanism is introduced to guide the selection of categorical generators, ensuring a balance between specialization, training stability, and computational efficiency, which is critical for 3D voxel processing. We evaluated the model's ability to generate and complete shapes with missing regions of varying sizes and compared its performance with state-of-the-art approaches. Both quantitative and qualitative results confirm the effectiveness of the proposed MoE-DCGAN in handling complex 3D data.