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Found 10 papers, 4 papers with code
PhysReaction: Physically Plausible Real-Time Humanoid Reaction Synthesis via Forward Dynamics Guided 4D Imitation
Humanoid Reaction Synthesis is pivotal for creating highly interactive and empathetic robots that can seamlessly integrate into human environments, enhancing the way we live, work, and communicate.
CrossVideo: Self-supervised Cross-modal Contrastive Learning for Point Cloud Video Understanding
This paper introduces a novel approach named CrossVideo, which aims to enhance self-supervised cross-modal contrastive learning in the field of point cloud video understanding.
Interactive Humanoid: Online Full-Body Motion Reaction Synthesis with Social Affordance Canonicalization and Forecasting
To support this task, we construct two datasets named HHI and CoChair and propose a unified method.
NSM4D: Neural Scene Model Based Online 4D Point Cloud Sequence Understanding
We integrate NSM4D with state-of-the-art 4D perception backbones, demonstrating significant improvements on various online perception benchmarks in indoor and outdoor settings.
LeaF: Learning Frames for 4D Point Cloud Sequence Understanding
With such frames, we can factorize geometry and motion to facilitate a feature-space geometric reconstruction for more effective 4D learning.
Complete-to-Partial 4D Distillation for Self-Supervised Point Cloud Sequence Representation Learning
Recent work on 4D point cloud sequences has attracted a lot of attention.
Point Primitive Transformer for Long-Term 4D Point Cloud Video Understanding
This paper proposes a 4D backbone for long-term point cloud video understanding.
HOI4D: A 4D Egocentric Dataset for Category-Level Human-Object Interaction
We present HOI4D, a large-scale 4D egocentric dataset with rich annotations, to catalyze the research of category-level human-object interaction.
Contrastive Multimodal Fusion with TupleInfoNCE
Another approach is to concatenate all the modalities into a tuple and then contrast positive and negative tuple correspondences.
Ranked #70 on
Semantic Segmentation
on NYU Depth v2
P4Contrast: Contrastive Learning with Pairs of Point-Pixel Pairs for RGB-D Scene Understanding
Self-supervised representation learning is a critical problem in computer vision, as it provides a way to pretrain feature extractors on large unlabeled datasets that can be used as an initialization for more efficient and effective training on downstream tasks.
