3d Shape Generation Achieves Reduced Memorization, Demonstrating Data’s Influence on Diversity

The increasing use of generative models in 3D vision raises a critical question, namely, do these systems truly create novel shapes or simply memorise those present in their training data? Shu Pu, Boya Zeng from Princeton University, Kaichen Zhou, and colleagues investigate this issue, developing a new framework to measure memorisation within 3D generative models, and revealing how different data characteristics and model designs influence this phenomenon. Their work demonstrates that memorisation is affected by the type of data used, increasing with both diversity and detailed conditioning, and is also linked to the strength of guidance signals during generation. Importantly, the team identifies strategies, such as employing longer latent vector sets and simple data augmentation techniques, that effectively reduce memorisation without compromising the quality of the generated 3D shapes, offering a path towards more robust and creative generative systems.

The researchers first apply their framework to quantify memorization in existing methods. Through controlled experiments with a latent vector-set (Vecset) diffusion model, they find that memorization depends on data modality, increasing with data diversity and finer-grained conditioning. On the modelling side, memorization peaks at a moderate guidance scale but can be mitigated by longer Vecsets and simple rotation augmentation. Together, this framework and analysis provide an empirical understanding of memorization in 3D generative models and suggest potential strategies for its control.

Vecset Improves 3D Shape Generalization

The provided excerpt focuses on research aimed at improving 3D shape generation models by reducing memorization of training data and enhancing generalization, enabling models to produce novel yet realistic shapes. The authors investigate a range of architectural and training techniques and evaluate their impact using quantitative metrics and visual analysis. The overall objective is to strike a balance between high-fidelity shape generation and avoiding overfitting, where generated outputs closely resemble examples seen during training.

A central technique explored in the study is Vecset, a representation that models 3D shapes as a set of latent queries combined with point cloud positional embeddings. The researchers experiment with different Vecset sequence lengths, specifically 768, 1024, and 1280. While changes in Vecset length do not significantly affect reconstruction quality, longer Vecsets tend to produce higher-fidelity shapes and show reduced memorization, suggesting improved generalization capacity. Another important factor examined is the guidance scale, which controls the strength of conditioning during generation. Higher guidance scales are shown to reduce memorization but can negatively affect prompt alignment, leading to shapes that are less faithful to the intended conditioning input.

The study also examines the robustness of distance metrics used to measure similarity between generated shapes and training data. Rotation augmentation experiments reveal that Latent Feature Distance (LFD) is sensitive to object rotation, whereas Uni3D demonstrates greater rotation invariance, particularly with respect to yaw rotations. This makes Uni3D more reliable for evaluating memorization and generalization. Additionally, the researchers compare conditional and unconditional generation, as well as multiple model architectures, including LAS-Diffusion, Wavelet Generation, 3DShape2VecSet, Michelangelo, and 3DTopia-XL.

The experimental results show clear differences in memorization behavior across models. Visualizations comparing generated shapes with their nearest training examples indicate that some models, such as NFD, unconditional LAS-Diffusion, and Wavelet Generation, exhibit strong memorization, often producing outputs that closely resemble training shapes even at moderate feature distances. In contrast, conditional LAS-Diffusion, 3DShape2VecSet, and Michelangelo generate shapes with more novel geometric features, reflecting stronger generalization. The study also finds that training on larger datasets generally promotes better generalization and reduces memorization. Among the evaluated models, 3DTopia-XL performs poorly in this regard, often producing low-quality shapes with limited diversity.

To quantify these observations, the authors use several metrics, including Chamfer Distance (CD) and F-Score to assess reconstruction quality, and LFD, Uni3D, and Nearest Feature Distance (NFD) to analyze similarity to training data and detect memorization. Rendering consistency is ensured by normalizing shapes to fit within a unit cube and rendering them in Blender at a resolution of 256×256, with randomized but fixed-seed camera poses and lighting.

Overall, the research highlights the trade-offs involved in 3D shape generation between realism and originality. By manipulating Vecset length, adjusting guidance scale, and selecting robust distance metrics, the authors demonstrate pathways toward models that generate high-quality, diverse 3D shapes while minimizing direct memorization of training data.

Quantifying Memorization in 3D Generative Models

Scientists have developed a new framework to quantify memorization in 3D generative models, addressing the critical question of whether these models truly create novel shapes or simply reproduce training data. The research team defined object-level memorization as the generation of shapes visually identical to those in the training set and model-level memorization as the generated set being closer to the training set than a held-out test set. Through benchmarking against human judgments, they determined that Light Field Distance (LFD) is the most accurate metric for identifying replicas of training shapes., Applying this framework to existing 3D generators, experiments revealed that models trained on limited datasets, such as single ShapeNet categories, demonstrate clear memorization, generating near-exact copies of training examples. Conversely, recent large-scale conditional generative models exhibit limited memorization and an enhanced capacity for generalization.

The team quantified memorization using a z-score, ZU, alongside Fréchet Distance to separate memorization from overall generation quality., Controlled experiments utilizing a latent vector-set (Vecset) diffusion model further illuminated the factors influencing memorization. Results demonstrate that rendered images are more prone to memorization than 3D shapes within the experimental setup, and that increased semantic diversity and finer-grained conditioning amplify memorization. From a modeling perspective, memorization peaked at a moderate classifier-free guidance scale, but could be mitigated by increasing the length of the latent Vecset and applying simple rotation augmentation. These findings suggest that rotation augmentation and longer Vecsets are effective strategies for reducing memorization without compromising generation quality, delivering a pathway towards more generalizable 3D synthesis.

Memorization Bias in 3D Generative Models

This research presents a new framework for evaluating memorization in three-dimensional generative models, addressing a critical question about how these systems learn to create novel shapes. Scientists quantitatively assessed the extent to which existing generators rely on memorizing training data, rather than genuinely synthesizing new forms, and discovered that models tend to memorize image data more readily than three-dimensional data. Through controlled experiments, the team identified factors influencing this memorization, including data diversity, conditioning granularity, and the scale of guidance used during generation., The findings demonstrate that increased data diversity and finer-grained control over the generation process can inadvertently lead to greater memorization, while moderate guidance scales also contribute to this effect. Importantly, the researchers propose practical strategies to mitigate memorization, such as employing longer latent Vecsets and incorporating simple rotation augmentations during training, all without compromising the quality of the generated outputs. The authors acknowledge that their controlled experiments were conducted on a relatively small-scale model and may not fully generalize to the most advanced architectures currently available. Future work should explore these findings in larger models and across a wider range of generative approaches, ultimately contributing to the design of three-dimensional generative models with improved generalization capabilities.