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DDMI: Domain-Agnostic Latent Diffusion Models for Synthesizing
  High-Quality Implicit Neural Representations

DDMI: Domain-Agnostic Latent Diffusion Models for Synthesizing High-Quality Implicit Neural Representations

23 January 2024
Dogyun Park
S. Kim
Sojin Lee
Hyunwoo J. Kim
    DiffM
ArXivPDFHTML

Papers citing "DDMI: Domain-Agnostic Latent Diffusion Models for Synthesizing High-Quality Implicit Neural Representations"

6 / 6 papers shown
Title
Improved Training Technique for Latent Consistency Models
Improved Training Technique for Latent Consistency Models
Quan Dao
Khanh Doan
Di Liu
Trung Le
Dimitris N. Metaxas
60
3
0
03 Feb 2025
From data to functa: Your data point is a function and you can treat it
  like one
From data to functa: Your data point is a function and you can treat it like one
Emilien Dupont
Hyunjik Kim
S. M. Ali Eslami
Danilo Jimenez Rezende
Dan Rosenbaum
TDI
3DPC
162
136
0
28 Jan 2022
VideoGPT: Video Generation using VQ-VAE and Transformers
VideoGPT: Video Generation using VQ-VAE and Transformers
Wilson Yan
Yunzhi Zhang
Pieter Abbeel
A. Srinivas
ViT
VGen
242
482
0
20 Apr 2021
Diffusion Probabilistic Models for 3D Point Cloud Generation
Diffusion Probabilistic Models for 3D Point Cloud Generation
Shitong Luo
Wei Hu
3DPC
172
711
0
02 Mar 2021
Is Space-Time Attention All You Need for Video Understanding?
Is Space-Time Attention All You Need for Video Understanding?
Gedas Bertasius
Heng Wang
Lorenzo Torresani
ViT
278
1,939
0
09 Feb 2021
PointNet: Deep Learning on Point Sets for 3D Classification and
  Segmentation
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
C. Qi
Hao Su
Kaichun Mo
Leonidas J. Guibas
3DH
3DPC
3DV
PINN
219
13,886
0
02 Dec 2016
1