Wavelet Latent Diffusion (Wala): Billion-Parameter 3D Generative Model
  with Compact Wavelet Encodings
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.
673478717adce85d6b29c398
Aditya Sanghi
extracted_pending
2024-11-13T09:59:14.593Z
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Aditya Sanghi
user
adityasanghi
673478717adce85d6b29c399
Aliasghar Khani
673478717adce85d6b29c39a
Pradyumna Reddy
673478717adce85d6b29c39b
Arianna Rampini
673478717adce85d6b29c39c
Derek Cheung
673478717adce85d6b29c39d
Kamal Rahimi Malekshan
673478717adce85d6b29c39e
Kanika Madan
673478717adce85d6b29c39f
Hooman Shayani
Wavelet Latent Diffusion (Wala): Billion-Parameter 3D Generative Model
  with Compact Wavelet Encodings
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.
Wavelet Latent Diffusion efficiently generates high-quality 3D shapes with high compression and minimal detail loss, improving performance and computational efficiency.
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