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Found 23 papers, 7 papers with code
Survey on Modeling of Articulated Objects
3D modeling of articulated objects is a research problem within computer vision, graphics, and robotics.
AFreeCA: Annotation-Free Counting for All
Consequently, we can generate counting data for any type of object and count them in an unsupervised manner.
AnaMoDiff: 2D Analogical Motion Diffusion via Disentangled Denoising
We present AnaMoDiff, a novel diffusion-based method for 2D motion analogies that is applied to raw, unannotated videos of articulated characters.
CAGE: Controllable Articulation GEneration
We address the challenge of generating 3D articulated objects in a controllable fashion.
Slice3D: Multi-Slice, Occlusion-Revealing, Single View 3D Reconstruction
Our key observation is that object slicing is more advantageous than altering views to reveal occluded structures.
CLiC: Concept Learning in Context
This paper addresses the challenge of learning a local visual pattern of an object from one image, and generating images depicting objects with that pattern.
SYRAC: Synthesize, Rank, and Count
To address this, we use latent diffusion models to create two types of synthetic data: one by removing pedestrians from real images, which generates ranked image pairs with a weak but reliable object quantity signal, and the other by generating synthetic images with a predetermined number of objects, offering a strong but noisy counting signal.
TExplain: Explaining Learned Visual Features via Pre-trained (Frozen) Language Models
These sentences are then used to extract the most frequent words, providing a comprehensive understanding of the learned features and patterns within the classifier.
PARIS: Part-level Reconstruction and Motion Analysis for Articulated Objects
Our approach improves reconstruction relative to state-of-the-art baselines with a Chamfer-L1 distance reduction of 3. 94 (45. 2%) for objects and 26. 79 (84. 5%) for parts, and achieves 5% error rate for motion estimation across 10 object categories.
BRICS: Bi-level feature Representation of Image CollectionS
Our key codes and feature grids are jointly trained continuously with well-defined gradient flows, leading to high usage rates of the feature grids and improved generative modeling compared to discrete Vector Quantization (VQ).
SKED: Sketch-guided Text-based 3D Editing
To ensure the generated output adheres to the provided sketches, we propose novel loss functions to generate the desired edits while preserving the density and radiance of the base instance.
DS-Fusion: Artistic Typography via Discriminated and Stylized Diffusion
We introduce a novel method to automatically generate an artistic typography by stylizing one or more letter fonts to visually convey the semantics of an input word, while ensuring that the output remains readable.
Cross-domain Compositing with Pretrained Diffusion Models
Diffusion models have enabled high-quality, conditional image editing capabilities.
MaskTune: Mitigating Spurious Correlations by Forcing to Explore
A fundamental challenge of over-parameterized deep learning models is learning meaningful data representations that yield good performance on a downstream task without over-fitting spurious input features.
Large-scale Building Damage Assessment using a Novel Hierarchical Transformer Architecture on Satellite Images
In this work, a novel transformer-based network is proposed for assessing building damage.
SAC-GAN: Structure-Aware Image Composition
Specifically, our network takes the semantic layout features from the input scene image, features encoded from the edges and silhouette in the input object patch, as well as a latent code as inputs, and generates a 2D spatial affine transform defining the translation and scaling of the object patch.
UNIST: Unpaired Neural Implicit Shape Translation Network
We introduce UNIST, the first deep neural implicit model for general-purpose, unpaired shape-to-shape translation, in both 2D and 3D domains.
Multimodal Shape Completion via IMLE
Shape completion is the problem of completing partial input shapes such as partial scans.
CAPRI-Net: Learning Compact CAD Shapes with Adaptive Primitive Assembly
We introduce CAPRI-Net, a neural network for learning compact and interpretable implicit representations of 3D computer-aided design (CAD) models, in the form of adaptive primitive assemblies.
RaidaR: A Rich Annotated Image Dataset of Rainy Street Scenes
We introduce RaidaR, a rich annotated image dataset of rainy street scenes, to support autonomous driving research.
Data to Physicalization: A Survey of the Physical Rendering Process
Physical representations of data offer physical and spatial ways of looking at, navigating, and interacting with data.
Graphics I.3.5; I.3.8
PIE-NET: Parametric Inference of Point Cloud Edges
We introduce an end-to-end learnable technique to robustly identify feature edges in 3D point cloud data.
Semi-Supervised Co-Analysis of 3D Shape Styles from Projected Lines
We present a semi-supervised co-analysis method for learning 3D shape styles from projected feature lines, achieving style patch localization with only weak supervision.
