Wavelet Latent Diffusion (Wala): Billion-Parameter 3D Generative Model
  with Compact Wavelet Encodings
Authors: name: Aditya Sanghi, name: Aliasghar Khani, name: Pradyumna Reddy, name: Arianna Rampini, name: Derek Cheung, name: Kamal Rahimi Malekshan, name: Kanika Madan, name: Hooman Shayani

Large-scale 3D generative models require substantial computational resources
yet often fall short in capturing fine details and complex geometries at high
resolutions. We attribute this limitation to the inefficiency of current
representations, which lack the compactness required to model the generative
models effectively. To address this, we introduce a novel approach called
Wavelet Latent Diffusion, or WaLa, that encodes 3D shapes into wavelet-based,
compact latent encodings. Specifically, we compress a 256^3 signed distance
field into a 12^3 times 4 latent grid, achieving an impressive 2427x
compression ratio with minimal loss of detail. This high level of compression
allows our method to efficiently train large-scale generative networks without
increasing the inference time. Our models, both conditional and unconditional,
contain approximately one billion parameters and successfully generate
high-quality 3D shapes at 256^3 resolution. Moreover, WaLa offers rapid
inference, producing shapes within two to four seconds depending on the
condition, despite the model's scale. We demonstrate state-of-the-art
performance across multiple datasets, with significant improvements in
generation quality, diversity, and computational efficiency. We open-source our
code and, to the best of our knowledge, release the largest pretrained 3D
generative models across different modalities.