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Found 22 papers, 7 papers with code
Flex3D: Feed-Forward 3D Generation With Flexible Reconstruction Model And Input View Curation
We employ a fine-tuned multi-view image diffusion model and a video diffusion model to generate a pool of candidate views, enabling a rich representation of the target 3D object.
The Llama 3 Herd of Models
This paper presents a new set of foundation models, called Llama 3.
Ranked #3 on
Multi-task Language Understanding
on MMLU
(using extra training data)
Meta 3D Gen
We introduce Meta 3D Gen (3DGen), a new state-of-the-art, fast pipeline for text-to-3D asset generation.
Meta 3D AssetGen: Text-to-Mesh Generation with High-Quality Geometry, Texture, and PBR Materials
We present Meta 3D AssetGen (AssetGen), a significant advancement in text-to-3D generation which produces faithful, high-quality meshes with texture and material control.
DreamCraft: Text-Guided Generation of Functional 3D Environments in Minecraft
In contrast, text-to-3D methods allow users to specify desired characteristics in natural language, offering a high amount of flexibility and expressivity.
VFusion3D: Learning Scalable 3D Generative Models from Video Diffusion Models
This results in a significant disparity in scale compared to the vast quantities of other types of data.
IM-3D: Iterative Multiview Diffusion and Reconstruction for High-Quality 3D Generation
A mitigation is to fine-tune the 2D generator to be multi-view aware, which can help distillation or can be combined with reconstruction networks to output 3D objects directly.
Replay: Multi-modal Multi-view Acted Videos for Casual Holography
We introduce Replay, a collection of multi-view, multi-modal videos of humans interacting socially.
Real-time volumetric rendering of dynamic humans
We present a method for fast 3D reconstruction and real-time rendering of dynamic humans from monocular videos with accompanying parametric body fits.
Text-To-4D Dynamic Scene Generation
We present MAV3D (Make-A-Video3D), a method for generating three-dimensional dynamic scenes from text descriptions.
Poly-NL: Linear Complexity Non-local Layers with Polynomials
Spatial self-attention layers, in the form of Non-Local blocks, introduce long-range dependencies in Convolutional Neural Networks by computing pairwise similarities among all possible positions.
Ranked #1 on
Face Detection
on WIDER Face (Hard)
To The Point: Correspondence-driven monocular 3D category reconstruction
We present To The Point (TTP), a method for reconstructing 3D objects from a single image using 2D to 3D correspondences learned from weak supervision.
Learning monocular 3D reconstruction of articulated categories from motion
Monocular 3D reconstruction of articulated object categories is challenging due to the lack of training data and the inherent ill-posedness of the problem.
Poly-NL: Linear Complexity Non-Local Layers With 3rd Order Polynomials
Spatial self-attention layers, in the form of Non-Local blocks, introduce long-range dependencies in Convolutional Neural Networks by computing pairwise similarities among all possible positions.
Microscopy Image Restoration with Deep Wiener-Kolmogorov filters
Microscopy is a powerful visualization tool in biology, enabling the study of cells, tissues, and the fundamental biological processes; yet, the observed images typically suffer from blur and background noise.
Pixel Adaptive Filtering Units
Next, we employ PAFU in deep neural networks as a replacement of standard convolutional layers to enhance the original architectures with spatially varying computations to achieve considerable performance improvements.
Iterative Residual CNNs for Burst Photography Applications
In this work, we focus on the fact that every frame of a burst sequence can be accurately described by a forward (physical) model.
Iterative Joint Image Demosaicking and Denoising using a Residual Denoising Network
Modern approaches try to jointly solve these problems, i. e. joint denoising-demosaicking which is an inherently ill-posed problem given that two-thirds of the intensity information is missing and the rest are perturbed by noise.
Deep Image Demosaicking using a Cascade of Convolutional Residual Denoising Networks
Demosaicking and denoising are among the most crucial steps of modern digital camera pipelines and their joint treatment is a highly ill-posed inverse problem where at-least two-thirds of the information are missing and the rest are corrupted by noise.
Tweester at SemEval-2017 Task 4: Fusion of Semantic-Affective and pairwise classification models for sentiment analysis in Twitter
In this paper, we describe our submission to SemEval2017 Task 4: Sentiment Analysis in Twitter.
Structural Attention Neural Networks for improved sentiment analysis
We introduce a tree-structured attention neural network for sentences and small phrases and apply it to the problem of sentiment classification.
