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Found 14 papers, 10 papers with code
SplatFormer: Point Transformer for Robust 3D Gaussian Splatting
To our knowledge, this is the first successful application of point transformers directly on 3DGS sets, surpassing the limitations of previous multi-scene training methods, which could handle only a restricted number of input views during inference.
FreSh: Frequency Shifting for Accelerated Neural Representation Learning
Implicit Neural Representations (INRs) have recently gained attention as a powerful approach for continuously representing signals such as images, videos, and 3D shapes using multilayer perceptrons (MLPs).
RISE-SDF: a Relightable Information-Shared Signed Distance Field for Glossy Object Inverse Rendering
Our experiments demonstrate that our algorithm achieves state-of-the-art performance in inverse rendering and relighting, with particularly strong results in the reconstruction of highly reflective objects.
SplatFields: Neural Gaussian Splats for Sparse 3D and 4D Reconstruction
Digitizing 3D static scenes and 4D dynamic events from multi-view images has long been a challenge in computer vision and graphics.
Degrees of Freedom Matter: Inferring Dynamics from Point Trajectories
By observing a set of point trajectories, we aim to learn an implicit motion field parameterized by a neural network to predict the movement of novel points within the same domain, without relying on any data-driven or scene-specific priors.
Creating a Digital Twin of Spinal Surgery: A Proof of Concept
Surgery digitalization is the process of creating a virtual replica of real-world surgery, also referred to as a surgical digital twin (SDT).
Morphable Diffusion: 3D-Consistent Diffusion for Single-image Avatar Creation
To the best of our knowledge, our proposed framework is the first diffusion model to enable the creation of fully 3D-consistent, animatable, and photorealistic human avatars from a single image of an unseen subject; extensive quantitative and qualitative evaluations demonstrate the advantages of our approach over existing state-of-the-art avatar creation models on both novel view and novel expression synthesis tasks.
ResFields: Residual Neural Fields for Spatiotemporal Signals
Neural fields, a category of neural networks trained to represent high-frequency signals, have gained significant attention in recent years due to their impressive performance in modeling complex 3D data, such as signed distance (SDFs) or radiance fields (NeRFs), via a single multi-layer perceptron (MLP).
Dynamic Point Fields
In this work, we present a dynamic point field model that combines the representational benefits of explicit point-based graphics with implicit deformation networks to allow efficient modeling of non-rigid 3D surfaces.
HARP: Personalized Hand Reconstruction from a Monocular RGB Video
We present HARP (HAnd Reconstruction and Personalization), a personalized hand avatar creation approach that takes a short monocular RGB video of a human hand as input and reconstructs a faithful hand avatar exhibiting a high-fidelity appearance and geometry.
SMPLpix: Neural Avatars from 3D Human Models
Recent advances in deep generative models have led to an unprecedented level of realism for synthetically generated images of humans.
Real Time Trajectory Prediction Using Deep Conditional Generative Models
Our method uses encoder and decoder deep networks that maps complete or partial trajectories to a Gaussian distributed latent space and back, allowing for fast inference of the future values of a trajectory given previous observations.
Efficient Learning on Point Clouds with Basis Point Sets
The basis point set representation is a residual representation that can be computed efficiently and can be used with standard neural network architectures and other machine learning algorithms.
Deep Directional Statistics: Pose Estimation with Uncertainty Quantification
However, in challenging imaging conditions such as on low-resolution images or when the image is corrupted by imaging artifacts, current systems degrade considerably in accuracy.
