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Found 29 papers, 14 papers with code
PersonaHOI: Effortlessly Improving Personalized Face with Human-Object Interaction Generation
We introduce PersonaHOI, a training- and tuning-free framework that fuses a general StableDiffusion model with a personalized face diffusion (PFD) model to generate identity-consistent human-object interaction (HOI) images.
Human-Object Interaction Detection
Human-Object Interaction Generation
MEt3R: Measuring Multi-View Consistency in Generated Images
We introduce MEt3R, a metric for multi-view consistency in generated images.
SLayR: Scene Layout Generation with Rectified Flow
We introduce SLayR, Scene Layout Generation with Rectified flow.
ContextGNN: Beyond Two-Tower Recommendation Systems
Recommendation systems predominantly utilize two-tower architectures, which evaluate user-item rankings through the inner product of their respective embeddings.
TokenFormer: Rethinking Transformer Scaling with Tokenized Model Parameters
By treating model parameters as tokens, we replace all the linear projections in Transformers with our token-parameter attention layer, where input tokens act as queries and model parameters as keys and values.
Spurfies: Sparse Surface Reconstruction using Local Geometry Priors
We introduce Spurfies, a novel method for sparse-view surface reconstruction that disentangles appearance and geometry information to utilize local geometry priors trained on synthetic data.
InterTrack: Tracking Human Object Interaction without Object Templates
In this paper, we present a method to track human object interaction without any object shape templates.
Scribbles for All: Benchmarking Scribble Supervised Segmentation Across Datasets
In this work, we introduce Scribbles for All, a label and training data generation algorithm for semantic segmentation trained on scribble labels.
Improving 2D Feature Representations by 3D-Aware Fine-Tuning
In this work, we show that fine-tuning on 3D-aware data improves the quality of emerging semantic features.
Sp2360: Sparse-view 360 Scene Reconstruction using Cascaded 2D Diffusion Priors
We aim to tackle sparse-view reconstruction of a 360 3D scene using priors from latent diffusion models (LDM).
GEARS: Local Geometry-aware Hand-object Interaction Synthesis
Generating realistic hand motion sequences in interaction with objects has gained increasing attention with the growing interest in digital humans.
From Similarity to Superiority: Channel Clustering for Time Series Forecasting
Motivated by our observation of a correlation between the time series model's performance boost against channel mixing and the intrinsic similarity on a pair of channels, we developed a novel and adaptable Channel Clustering Module (CCM).
latentSplat: Autoencoding Variational Gaussians for Fast Generalizable 3D Reconstruction
We present latentSplat, a method to predict semantic Gaussians in a 3D latent space that can be splatted and decoded by a light-weight generative 2D architecture.
Recent Trends in 3D Reconstruction of General Non-Rigid Scenes
Reconstructing models of the real world, including 3D geometry, appearance, and motion of real scenes, is essential for computer graphics and computer vision.
NRDF: Neural Riemannian Distance Fields for Learning Articulated Pose Priors
Faithfully modeling the space of articulations is a crucial task that allows recovery and generation of realistic poses, and remains a notorious challenge.
Neural Point Cloud Diffusion for Disentangled 3D Shape and Appearance Generation
However, none of the existing models enable disentangled generation to control the shape and appearance separately.
Template Free Reconstruction of Human-object Interaction with Procedural Interaction Generation
We generate 1M+ human-object interaction pairs in 3D and leverage this large-scale data to train our HDM (Hierarchical Diffusion Model), a novel method to reconstruct interacting human and unseen objects, without any templates.
Relational Deep Learning: Graph Representation Learning on Relational Databases
The core idea is to view relational databases as a temporal, heterogeneous graph, with a node for each row in each table, and edges specified by primary-foreign key links.
Neural Parametric Gaussians for Monocular Non-Rigid Object Reconstruction
However, owing to the ill-posed nature of this problem, there has been no solution that can provide consistent, high-quality novel views from camera positions that are significantly different from the training views.
SimNP: Learning Self-Similarity Priors Between Neural Points
(1) We design the first neural point representation on a category level by utilizing the concept of coherent point clouds.
Pose-NDF: Modeling Human Pose Manifolds with Neural Distance Fields
The resulting high-dimensional implicit function can be differentiated with respect to the input poses and thus can be used to project arbitrary poses onto the manifold by using gradient descent on the set of 3-dimensional hyperspheres.
Deep Local Shapes: Learning Local SDF Priors for Detailed 3D Reconstruction
Efficiently reconstructing complex and intricate surfaces at scale is a long-standing goal in machine perception.
Quaternion Equivariant Capsule Networks for 3D Point Clouds
We present a 3D capsule module for processing point clouds that is equivariant to 3D rotations and translations, as well as invariant to permutations of the input points.
Deep Iterative Surface Normal Estimation
This results in a state-of-the-art surface normal estimator that is robust to noise, outliers and point density variation, preserves sharp features through anisotropic kernels and equivariance through a local quaternion-based spatial transformer.
Ranked #7 on
Surface Normals Estimation
on PCPNet
Fast Graph Representation Learning with PyTorch Geometric
We introduce PyTorch Geometric, a library for deep learning on irregularly structured input data such as graphs, point clouds and manifolds, built upon PyTorch.
Ranked #4 on
Graph Classification
on REDDIT-B
Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks
We show that GNNs have the same expressiveness as the $1$-WL in terms of distinguishing non-isomorphic (sub-)graphs.
Ranked #5 on
Graph Classification
on NCI1
Group Equivariant Capsule Networks
We present group equivariant capsule networks, a framework to introduce guaranteed equivariance and invariance properties to the capsule network idea.
SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels
We present Spline-based Convolutional Neural Networks (SplineCNNs), a variant of deep neural networks for irregular structured and geometric input, e. g., graphs or meshes.
Ranked #2 on
Node Classification
on Cora
