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Found 14 papers, 2 papers with code
Differentiable Visual Computing for Inverse Problems and Machine Learning
This approach is predicated by neural network differentiability, the requirement that analytic derivatives of a given problem's task metric can be computed with respect to neural network's parameters.
FrePolad: Frequency-Rectified Point Latent Diffusion for Point Cloud Generation
We propose FrePolad: frequency-rectified point latent diffusion, a point cloud generation pipeline integrating a variational autoencoder (VAE) with a denoising diffusion probabilistic model (DDPM) for the latent distribution.
ARF-Plus: Controlling Perceptual Factors in Artistic Radiance Fields for 3D Scene Stylization
We highlight a research gap in radiance fields style transfer, the lack of sufficient perceptual controllability, motivated by the existing concept in the 2D image style transfer.
Perceptual Quality Assessment of NeRF and Neural View Synthesis Methods for Front-Facing Views
We measured the quality of videos synthesized by several NVS methods in a well-controlled perceptual quality assessment experiment as well as with many existing state-of-the-art image/video quality metrics.
$α$Surf: Implicit Surface Reconstruction for Semi-Transparent and Thin Objects with Decoupled Geometry and Opacity
While neural radiance field (NeRF) based methods can model semi-transparency and achieve photo-realistic quality in synthesized novel views, their volumetric geometry representation tightly couples geometry and opacity, and therefore cannot be easily converted into surfaces without introducing artifacts.
3D GAN Inversion with Facial Symmetry Prior
It is natural to associate 3D GANs with GAN inversion methods to project a real image into the generator's latent space, allowing free-view consistent synthesis and editing, referred as 3D GAN inversion.
CUF: Continuous Upsampling Filters
Neural fields have rapidly been adopted for representing 3D signals, but their application to more classical 2D image-processing has been relatively limited.
CLIP-PAE: Projection-Augmentation Embedding to Extract Relevant Features for a Disentangled, Interpretable, and Controllable Text-Guided Face Manipulation
We introduce CLIP Projection-Augmentation Embedding (PAE) as an optimization target to improve the performance of text-guided image manipulation.
One-shot Detail Retouching with Patch Space Neural Transformation Blending
Our method accurately transfers complex detail retouching edits.
D$^2$NeRF: Self-Supervised Decoupling of Dynamic and Static Objects from a Monocular Video
We introduce Decoupled Dynamic Neural Radiance Field (D$^2$NeRF), a self-supervised approach that takes a monocular video and learns a 3D scene representation which decouples moving objects, including their shadows, from the static background.
Kubric: A scalable dataset generator
Data is the driving force of machine learning, with the amount and quality of training data often being more important for the performance of a system than architecture and training details.
Iso-Points: Optimizing Neural Implicit Surfaces with Hybrid Representations
Neural implicit functions have emerged as a powerful representation for surfaces in 3D.
A Network Architecture for Point Cloud Classification via Automatic Depth Images Generation
We propose a novel neural network architecture for point cloud classification.
Human Shape From Silhouettes Using Generative HKS Descriptors and Cross-Modal Neural Networks
In this work, we present a novel method for capturing human body shape from a single scaled silhouette.
