TLDR: Scaffold Diffusion is a novel generative model designed to create realistic, sparse, multi-category 3D voxel structures. It overcomes challenges like cubic memory scaling and class imbalance by treating voxels as tokens and employing a discrete diffusion language model with 3D positional encoding. The model successfully generates spatially coherent and functional structures, even from data with over 98% sparsity, outperforming previous baselines, as demonstrated with Minecraft house generation. This work extends discrete diffusion’s applicability from sequential data to complex 3D spatial domains.
Generating realistic and complex 3D structures, especially those made of voxels (3D pixels), has long been a significant challenge in fields ranging from computer vision and robotics to game development. The primary hurdles include the immense memory requirements that scale cubically with structure size and the significant class imbalance caused by sparsity, where most of the 3D space is empty.
A new generative model called Scaffold Diffusion has emerged to address these very challenges. Developed by Justin Jung, Scaffold Diffusion is specifically designed for creating sparse, multi-category 3D voxel structures. It introduces a novel approach by treating individual voxels as tokens, similar to words in a sentence, and then uses a discrete diffusion language model to generate these intricate 3D forms. This innovative method demonstrates that discrete diffusion language models can be effectively extended beyond traditional sequential domains like text to generate spatially coherent 3D structures.
The effectiveness of Scaffold Diffusion was rigorously evaluated on Minecraft house structures from the 3D-Craft dataset. The results were compelling: unlike previous baseline models and even an auto-regressive formulation, Scaffold Diffusion consistently produced realistic and coherent structures. This was achieved even when the model was trained on data with over 98% sparsity, a testament to its robust design in handling significant class imbalance.
How Scaffold Diffusion Works
At its core, Scaffold Diffusion formulates multi-category voxels as tokens. It integrates 3D positional encoding into a masked diffusion language model (MDLM), which is crucial for ensuring the generated structures are spatially aware and coherent. This means the model understands where each voxel should be placed in 3D space relative to others. The process involves conditioning on a boolean occupancy map, which defines the non-background voxel positions, and then generating the specific categories for these occupied voxels. The model extracts only the occupied voxels and their locations, converting them into a sequence for the MDLM to process, thereby circumventing the memory and class imbalance issues associated with working on the full, sparse voxel map.
Outperforming Existing Methods
The research highlights that Scaffold Diffusion significantly outperforms existing methods. For instance, an autoregressive baseline often generated collapsed structures dominated by a few block types or featured implausible block placements. Another comparable model, a VQ-VAE latent multinomial diffusion model, suffered from an over-representation of background voxels, even with techniques to mitigate class imbalance. Scaffold Diffusion, in contrast, consistently generates functional and visually plausible 3D structures.
The importance of 3D positional information was also demonstrated through ablations, showing that fixed 3D sinusoidal positional embeddings were far more effective than learned embeddings for generating spatially coherent structures.
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Future Directions and Interactive Exploration
While Scaffold Diffusion marks a significant leap, the authors acknowledge areas for future work. Currently, the model relies on a pre-defined boolean occupancy map. Future research could explore training a generative model to first create these occupancy maps, leading to a fully generative two-stage process. Additionally, the number of active voxels is limited by the sequence length of the discrete diffusion model, suggesting hierarchical generation as an interesting direction for creating much larger voxel structures.
To allow readers to experience the model’s capabilities firsthand, an interactive viewer has been made available. This demo allows users to visualize generated samples and observe the generation process in action. You can explore the demo at scaffold.deepexploration.org.
In conclusion, Scaffold Diffusion presents a promising framework for 3D sparse voxel generative modeling, extending the practical applicability of discrete diffusion beyond naturally sequential data into the complex and challenging 3D spatial domain. For more details, you can refer to the full research paper: Scaffold Diffusion: Sparse Multi-Category Voxel Structure Generation with Discrete Diffusion.
