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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,657 papers · 148 categories

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4 results for “3D-Aware”

Likelihood-Based Generative Radiance Field with Latent Space Energy-Based Model for 3D-Aware Disentangled Image Representationcs.CV

Proposes a model to generate 3D-aware images from 2D images.

problem Generating 3D-aware images from 2D images.

method Likelihood-based top-down model using Neural Radiance Fields and energy-based latent variables.

result Model can infer 3D object structures from 2D images and generate novel views.

NeRF Radiance Fields Energy-based Models 3D-Aware Image Generation

2023-04-16abs ↗pdf ↗

StyleNeRF: A Style-based 3D-Aware Generator for High-resolution Image Synthesiscs.CV

StyleNeRF generates high-resolution images with 3D consistency and style control.

problem Generating high-resolution images with fine details and 3D consistency.

method Integrates NeRF into a style-based generator for efficient high-resolution image synthesis.

result Synthesizes high-resolution images at interactive rates with high 3D consistency and style control.

NeRF 3D-Aware Image Synthesis High-Resolution Style Control

2021-10-18abs ↗pdf ↗

Multi-Plane Program Induction with 3D Box Priorscs.CV

BPI models 2D patterns on multiple planes and 3D scene from a single image.

problem Understanding and editing images with multiple 2D planes and 3D scene from a single image.

method Box Program Induction (BPI) with neural networks and search-based algorithm.

result Holistic, structured scene representation enables 3D-aware image editing.

image editing 3D scene program induction neural networks search-based algorithm

2020-11-19abs ↗pdf ↗

3D pride without 2D prejudice: Bias-controlled multi-level generative models for structure-based ligand designstat.ML

Improved 3D generative models for drug design reduce bias and enhance data efficiency.

problem Data sparsity and bias in 3D molecular design models.

method Multi-level contrastive learning protocol for bias control and data efficiency.

result Hierarchical generative models that are topologically unbiased and explainable.

3D generative models drug design bias control multi-level learning contrastive learning

2022-04-22abs ↗pdf ↗