GOEnFusion: gradient origin encodings for 3D forward diffusion models

GOEnFusion: gradient origin encodings for 3D forward diffusion models

The recently introduced Forward-Diffusion method allows to train a 3D diffusion model using only 2D images for supervision. However, it does not easily generalise to different 3D representations and requires a computationally expensive auto-regressive sampling process to generate the underlying 3D s...

Full beskrivning

Bibliografiska uppgifter
Huvudupphovsmän:	Karnewar, A, Vedaldi, A, Mitra, NJ, Novotny, D
Materialtyp:	Internet publication
Språk:	English
Publicerad:	2023

Liknande verk

HoloDiffusion: training a 3D diffusion model using 2D Images
av: Karnewar, A, et al.
Publicerad: (2023)

The Effect of Diferentiation, Price, and Facility on Customers’ Satisfaction (Case Study on Goen Authentic In East Jakarta)
av: Siti Mariam, et al.
Publicerad: (2022-04-01)

Publicist voor Oranje. R. M. van Goens en De ouderwetse Nederlandsche patriot (1781-1783)
av: J.M. Peterse
Publicerad: (1988-01-01)

Publicist voor Oranje. R. M. van Goens en De ouderwetse Nederlandsche patriot (1781-1783)
av: J.M. Peterse
Publicerad: (1988-01-01)

Diffusion phase-imaging in anisotropic media using non-linear gradients for diffusion encoding.
av: Pamela Wochner, et al.
Publicerad: (2023-01-01)

Diffusion phase-imaging in anisotropic media using non-linear gradients for diffusion encoding
av: Pamela Wochner, et al.
Publicerad: (2023-01-01)

Moving forward in structure from motion
av: Vedaldi, A, et al.
Publicerad: (2007)

Catfree3d: category-agnostic 3D object detection with diffusion
av: Bian, W, et al.
Publicerad: (2024)

Convolution encoder for forward error correction
av: Ahmad Termizi, Mohd Azmi
Publicerad: (2010)

3-D resistivity forward modeling and inversion using conjugate gradients
av: Zhang, Jie, et al.
Publicerad: (2017)

Viewset diffusion: (0-)image-conditioned 3D generative models from 2D data
av: Szymanowicz, S, et al.
Publicerad: (2023)

Viewset Diffusion: (0-)image-conditioned 3D generative models from 2D data
av: Szymanowicz, S, et al.
Publicerad: (2024)

Learning 3D object categories by looking around them
av: Novotny, D, et al.
Publicerad: (2017)

Taking visual motion prediction to new heightfields
av: Ehrhardt, S, et al.
Publicerad: (2019)

Flex3D: feed-forward 3D generation with flexible reconstruction model and input view curation
av: Han, J, et al.
Publicerad: (2024)

Farm3D: learning articulated 3D animals by distilling 2D diffusion
av: Jakab, T, et al.
Publicerad: (2024)

Mitigating Peripheral Nerve Stimulation in Cardiac Diffusion Tensor Imaging (cDTI) with Time-optimal Diffusion Encoding Gradient Waveform Design
av: Ariel Hannum, MSc, et al.
Publicerad: (2024-01-01)

Canonical 3D Deformer Maps: unifying parametric and non-parametric methods for dense weakly-supervised category reconstruction
av: Novotny, D, et al.
Publicerad: (2020)

It takes (only) two: adversarial generator-encoder networks
av: Ulyanov, D, et al.
Publicerad: (2018)

Flash3D: feed-forward generalisable 3D scene reconstruction from a single image
av: Szymanowicz, S, et al.
Publicerad: (2024)

The dot-compartment revealed? Diffusion MRI with ultra-strong gradients and spherical tensor encoding in the living human brain
av: Chantal M.W. Tax, et al.
Publicerad: (2020-04-01)

Neural feature fusion fields: 3D distillation of self-supervised 2D image representations
av: Tschernezki, V, et al.
Publicerad: (2023)

The devil is in the details: an evaluation of recent feature encoding methods
av: Chatfield, K, et al.
Publicerad: (2011)

Net2Vec: Quantifying and explaining how concepts are encoded by filters in deep neural networks
av: Fong, R, et al.
Publicerad: (2018)

Learning feed-forward one-shot learners
av: Bertinetto, L, et al.
Publicerad: (2016)

PC2: projection-conditioned point cloud diffusion for single-image 3D reconstruction
av: Melas-Kyriazi, L, et al.
Publicerad: (2023)

Texture networks: Feed-forward synthesis of textures and stylized images
av: Ulyanov, D, et al.
Publicerad: (2016)

Improved texture networks: Maximizing quality and diversity in feed-forward stylization and texture synthesis
av: Ulyanov, D, et al.
Publicerad: (2017)

Learning Gradient-Based Feed-Forward Equalizer for VCSELs
av: Muralikrishnan Srinivasan, et al.
Publicerad: (2024-10-01)

Contrastive lift: 3D object instance segmentation by slow-fast contrastive fusion
av: Bhalgat, Y, et al.
Publicerad: (2024)

Contribution of rotational diffusion to pulsed field gradient diffusion measurements.
av: Baldwin, A, et al.
Publicerad: (2007)

3D Multi-bodies: fitting sets of plausible 3D human models to ambiguous image data
av: Biggs, B, et al.
Publicerad: (2020)

Pore size estimation from double diffusion encoding
av: Methot Vincent, et al.
Publicerad: (2017-09-01)

IM-3D: iterative multiview diffusion and reconstruction for high-quality 3D generation
av: Melas-Kyriazi, L, et al.
Publicerad: (2024)

Encoding the Intensity and Phase Gradient of Light Beams with Arbitrary Shapes
av: Alejandra Serrano-Trujillo, et al.
Publicerad: (2023-03-01)

Spatio-temporal encoding by quadratic gradients in magnetic resonance imaging
av: Sina Marhabaie, et al.
Publicerad: (2020-12-01)

DensePose 3D: lifting canonical surface maps of articulated objects to the third dimension
av: Shapovalov, R, et al.
Publicerad: (2022)

C3DPO: Canonical 3D pose networks for non-rigid structure from motion
av: Novotny, D, et al.
Publicerad: (2020)

Diffusion models for open-vocabulary segmentation
av: Karazija, L, et al.
Publicerad: (2023)

Diffusion models for open-vocabulary segmentation
av: Karazija, L, et al.
Publicerad: (2024)