What will the future be? We wonder! In this survey, we explore the gap between current
research in egocentric vision and the ever-anticipated future, where wearable computing …

Recent work on 4D point cloud sequences has attracted a lot of attention. However,
obtaining exhaustively labeled 4D datasets is often very expensive and laborious, so it is …

This paper presents ShapeLLM, the first 3D Multimodal Large Language Model (LLM)
designed for embodied interaction, exploring a universal 3D object understanding with 3D …

We focus on learning descriptive geometry and motion features from 4D point cloud
sequences in this work. Existing works usually develop generic 4D learning tools without …

Self-supervised learning can extract representations of good quality from solely unlabeled
data, which is appealing for point cloud videos due to their high labelling cost. In this paper …

Recently, the community has made tremendous progress in developing effective methods
for point cloud video understanding that learn from massive amounts of labeled data …

We propose a unified point cloud video self-supervised learning framework for object-centric
and scene-centric data. Previous methods commonly conduct representation learning at the …

We study a new problem of semantic complete scene forecasting (SCSF) in this work. Given
a 4D dynamic point cloud sequence, our goal is to forecast the complete scene …

We present a new self-supervised paradigm on point cloud sequence understanding.
Inspired by the discriminative and generative self-supervised methods, we design two tasks …

We focus on the human-humanoid interaction task optionally with an object. We propose a
new task named online full-body motion reaction synthesis, which generates humanoid …