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Found 9 papers, 4 papers with code
HOI-Diff: Text-Driven Synthesis of 3D Human-Object Interactions using Diffusion Models
We also develop an affordance prediction diffusion model (APDM) to predict the contacting area between the human and object during the interactions driven by the textual prompt.
The MI-Motion Dataset and Benchmark for 3D Multi-Person Motion Prediction
We believe that the proposed MI-Motion benchmark dataset and baseline will facilitate future research in this area, ultimately leading to better understanding and modeling of multi-person interactions.
Learning Weakly Supervised Audio-Visual Violence Detection in Hyperbolic Space
To overcome this, we propose HyperVD, a novel framework that learns snippet embeddings in hyperbolic space to improve model discrimination.
Trajectory-Aware Body Interaction Transformer for Multi-Person Pose Forecasting
Specifically, we construct a Temporal Body Partition Module that transforms all the pose sequences into a Multi-Person Body-Part sequence to retain spatial and temporal information based on body semantics.
PointCMC: Cross-Modal Multi-Scale Correspondences Learning for Point Cloud Understanding
To solve it, we proposed PointCMC, a novel cross-modal method to model multi-scale correspondences across modalities for self-supervised point cloud representation learning.
SoMoFormer: Social-Aware Motion Transformer for Multi-Person Motion Prediction
Most prior methods only involve learning local pose dynamics for individual motion (without global body trajectory) and also struggle to capture complex interaction dependencies for social interactions.
Upsampling Autoencoder for Self-Supervised Point Cloud Learning
In computer-aided design (CAD) community, the point cloud data is pervasively applied in reverse engineering, where the point cloud analysis plays an important role.
PatchFormer: An Efficient Point Transformer with Patch Attention
Extensive experiments demonstrate that our network achieves comparable accuracy on general point cloud learning tasks with 9. 2x speed-up than previous point Transformers.
Ranked #1 on
Semantic Segmentation
on ShapeNet
PVT: Point-Voxel Transformer for Point Cloud Learning
The recently developed pure Transformer architectures have attained promising accuracy on point cloud learning benchmarks compared to convolutional neural networks.
Ranked #18 on
3D Part Segmentation
on ShapeNet-Part
