title: Wavelet Latent Diffusion (Wala): Billion-Parameter 3D Generative Model
    with Compact Wavelet Encodings
summary: 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.
paper_authors._id: 673478717adce85d6b29c398
paper_authors.name: Aditya Sanghi
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2024-11-13T09:59:14.593Z
paper_authors._id: 633f75ce7f69125dcea1adad
paper_authors.avatarUrl: /avatars/6eb040568f7c6957f36e232f0d7fec98.svg
paper_authors.fullname: Aditya Sanghi
paper_authors.type: user
paper_authors.user: adityasanghi
paper_authors._id: 673478717adce85d6b29c399
paper_authors.name: Aliasghar Khani
paper_authors._id: 673478717adce85d6b29c39a
paper_authors.name: Pradyumna Reddy
paper_authors._id: 673478717adce85d6b29c39b
paper_authors.name: Arianna Rampini
paper_authors._id: 673478717adce85d6b29c39c
paper_authors.name: Derek Cheung
paper_authors._id: 673478717adce85d6b29c39d
paper_authors.name: Kamal Rahimi Malekshan
paper_authors._id: 673478717adce85d6b29c39e
paper_authors.name: Kanika Madan
paper_authors._id: 673478717adce85d6b29c39f
paper_authors.name: Hooman Shayani
paper_title: Wavelet Latent Diffusion (Wala): Billion-Parameter 3D Generative Model
    with Compact Wavelet Encodings
paper_summary: 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.
paper_ai_summary: Wavelet Latent Diffusion efficiently generates high-quality 3D shapes with high compression and minimal detail loss, improving performance and computational efficiency.
paper_ai_keywords: wavelet Latent Diffusion WaLa wavelet-based latent encodings signed distance field latent grid generative networks conditional models unconditional models inference time generation quality diversity computational efficiency pretrained 3D generative models