Search Results for author:
Found 16 papers, 7 papers with code
MultiPhys: Multi-Person Physics-aware 3D Motion Estimation
We introduce MultiPhys, a method designed for recovering multi-person motion from monocular videos.
CAD: Photorealistic 3D Generation via Adversarial Distillation
The increased demand for 3D data in AR/VR, robotics and gaming applications, gave rise to powerful generative pipelines capable of synthesizing high-quality 3D objects.
CC3D: Layout-Conditioned Generation of Compositional 3D Scenes
In this work, we introduce CC3D, a conditional generative model that synthesizes complex 3D scenes conditioned on 2D semantic scene layouts, trained using single-view images.
CurveCloudNet: Processing Point Clouds with 1D Structure
Modern depth sensors such as LiDAR operate by sweeping laser-beams across the scene, resulting in a point cloud with notable 1D curve-like structures.
PartNeRF: Generating Part-Aware Editable 3D Shapes without 3D Supervision
Evaluations on various ShapeNet categories demonstrate the ability of our model to generate editable 3D objects of improved fidelity, compared to previous part-based generative approaches that require 3D supervision or models relying on NeRFs.
Generating Part-Aware Editable 3D Shapes Without 3D Supervision
Evaluations on various ShapeNet categories demonstrate the ability of our model to generate editable 3D objects of improved fidelity, compared to previous part-based generative approaches that require 3D supervision or models relying on NeRFs.
ALTO: Alternating Latent Topologies for Implicit 3D Reconstruction
One school of thought is to encode a latent vector for each point (point latents).
COPILOT: Human-Environment Collision Prediction and Localization from Egocentric Videos
In this work, we introduce the challenging problem of predicting collisions in diverse environments from multi-view egocentric videos captured from body-mounted cameras.
3D-Aware Video Generation
Generative models have emerged as an essential building block for many image synthesis and editing tasks.
ATISS: Autoregressive Transformers for Indoor Scene Synthesis
The ability to synthesize realistic and diverse indoor furniture layouts automatically or based on partial input, unlocks many applications, from better interactive 3D tools to data synthesis for training and simulation.
Ranked #3 on
Indoor Scene Synthesis
on PRO-teXt
2D Semantic Segmentation task 1 (8 classes)
3D Semantic Scene Completion
+1
Neural Parts: Learning Expressive 3D Shape Abstractions with Invertible Neural Networks
The INN allows us to compute the inverse mapping of the homeomorphism, which in turn, enables the efficient computation of both the implicit surface function of a primitive and its mesh, without any additional post-processing.
Learning Unsupervised Hierarchical Part Decomposition of 3D Objects from a Single RGB Image
Humans perceive the 3D world as a set of distinct objects that are characterized by various low-level (geometry, reflectance) and high-level (connectivity, adjacency, symmetry) properties.
Superquadrics Revisited: Learning 3D Shape Parsing beyond Cuboids
Abstracting complex 3D shapes with parsimonious part-based representations has been a long standing goal in computer vision.
RayNet: Learning Volumetric 3D Reconstruction with Ray Potentials
RayNet integrates a CNN that learns view-invariant feature representations with an MRF that explicitly encodes the physics of perspective projection and occlusion.
PointFlowNet: Learning Representations for Rigid Motion Estimation from Point Clouds
In this paper, we propose to estimate 3D motion from such unstructured point clouds using a deep neural network.
Learning Local Feature Aggregation Functions with Backpropagation
This paper introduces a family of local feature aggregation functions and a novel method to estimate their parameters, such that they generate optimal representations for classification (or any task that can be expressed as a cost function minimization problem).
