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Found 10 papers, 4 papers with code
VoxNeRF: Bridging Voxel Representation and Neural Radiance Fields for Enhanced Indoor View Synthesis
Creating high-quality view synthesis is essential for immersive applications but continues to be problematic, particularly in indoor environments and for real-time deployment.
Fg-T2M: Fine-Grained Text-Driven Human Motion Generation via Diffusion Model
Text-driven human motion generation in computer vision is both significant and challenging.
Ranked #13 on
Motion Synthesis
on KIT Motion-Language
Dynamic Hyperbolic Attention Network for Fine Hand-object Reconstruction
In this work, we propose the first precise hand-object reconstruction method in hyperbolic space, namely Dynamic Hyperbolic Attention Network (DHANet), which leverages intrinsic properties of hyperbolic space to learn representative features.
CommonScenes: Generating Commonsense 3D Indoor Scenes with Scene Graph Diffusion
The generated scenes can be manipulated by editing the input scene graph and sampling the noise in the diffusion model.
Incremental 3D Semantic Scene Graph Prediction from RGB Sequences
Our method consists of a novel incremental entity estimation pipeline and a scene graph prediction network.
HouseCat6D -- A Large-Scale Multi-Modal Category Level 6D Object Perception Dataset with Household Objects in Realistic Scenarios
Estimating 6D object poses is a major challenge in 3D computer vision.
MonoGraspNet: 6-DoF Grasping with a Single RGB Image
6-DoF robotic grasping is a long-lasting but unsolved problem.
Bending Graphs: Hierarchical Shape Matching using Gated Optimal Transport
Shape matching has been a long-studied problem for the computer graphics and vision community.
SceneGraphFusion: Incremental 3D Scene Graph Prediction from RGB-D Sequences
Scene graphs are a compact and explicit representation successfully used in a variety of 2D scene understanding tasks.
Ranked #1 on
3D Object Classification
on 3R-Scan
SCFusion: Real-time Incremental Scene Reconstruction with Semantic Completion
We propose a framework that ameliorates this issue by performing scene reconstruction and semantic scene completion jointly in an incremental and real-time manner, based on an input sequence of depth maps.
