53 citations · 69 across the 12 of their papers we have counts for
14 papers
AnyWorld: Factorized Egocentric World Models for Cross-Embodiment Generalization
Cheng Chen, Jerry Bai, Jiacheng Wei +11
Collecting contact-rich robot experiences at scale remains a major bottleneck for generalizable manipulation. Beyond data quantity, robot learning also requires diverse experiences…
Synthetic-to-Real Translation for Class-Agnostic Motion Prediction
Yizheng Wu, Hongwei Fan, Kewei Wang +8
Motion understanding is critical for ensuring safety and robustness in autonomous driving systems, driving increasing interest in motion prediction. A key challenge in this domain…
IC-World: In-Context Generation for Shared World Modeling
Fan Wu, Jiacheng Wei, Ruibo Li +4
Video-based world models have recently garnered increasing attention for their ability to synthesize diverse and dynamic visual environments. In this paper, we focus on shared worl…
Weakly and Self-Supervised Class-Agnostic Motion Prediction for Autonomous Driving
Ruibo Li, Hanyu Shi, Zhe Wang +1
Understanding motion in dynamic environments is critical for autonomous driving, thereby motivating research on class-agnostic motion prediction. In this work, we investigate weakl…
DSOcc: Leveraging Depth Awareness and Semantic Aid to Boost Camera-Based 3D Semantic Occupancy Prediction
Naiyu Fang, Zheyuan Zhou, Kang Wang +5
Camera-based 3D semantic occupancy prediction offers an efficient and cost-effective solution for perceiving surrounding scenes in autonomous driving. However, existing works rely…
TacoDepth: Towards Efficient Radar-Camera Depth Estimation with One-stage Fusion
Yiran Wang, Jiaqi Li, Chaoyi Hong +6
Radar-Camera depth estimation aims to predict dense and accurate metric depth by fusing input images and Radar data. Model efficiency is crucial for this task in pursuit of real-ti…
Self-Supervised 3D Scene Flow Estimation and Motion Prediction using Local Rigidity Prior
Ruibo Li, Chi Zhang, Zhe Wang +2
In this article, we investigate self-supervised 3D scene flow estimation and class-agnostic motion prediction on point clouds. A realistic scene can be well modeled as a collection…
Unsupervised 3D Pose Transfer with Cross Consistency and Dual Reconstruction
Chaoyue Song, Jiacheng Wei, Ruibo Li +2
The goal of 3D pose transfer is to transfer the pose from the source mesh to the target mesh while preserving the identity information (e.g., face, body shape) of the target mesh.…
Collaborative Propagation on Multiple Instance Graphs for 3D Instance Segmentation with Single-point Supervision
Shichao Dong, Ruibo Li, Jiacheng Wei +2
Instance segmentation on 3D point clouds has been attracting increasing attention due to its wide applications, especially in scene understanding areas. However, most existing meth…
Efficient Few-Shot Object Detection via Knowledge Inheritance
Ze Yang, Chi Zhang, Ruibo Li +2
Few-shot object detection (FSOD), which aims at learning a generic detector that can adapt to unseen tasks with scarce training samples, has witnessed consistent improvement recent…
Meta Navigator: Search for a Good Adaptation Policy for Few-shot Learning
Chi Zhang, Henghui Ding, Guosheng Lin +3
Few-shot learning aims to adapt knowledge learned from previous tasks to novel tasks with only a limited amount of labeled data. Research literature on few-shot learning exhibits g…
3D Pose Transfer with Correspondence Learning and Mesh Refinement
Chaoyue Song, Jiacheng Wei, Ruibo Li +2
3D pose transfer is one of the most challenging 3D generation tasks. It aims to transfer the pose of a source mesh to a target mesh and keep the identity (e.g., body shape) of the…
Self-Point-Flow: Self-Supervised Scene Flow Estimation from Point Clouds with Optimal Transport and Random Walk
Ruibo Li, Guosheng Lin, Lihua Xie
Due to the scarcity of annotated scene flow data, self-supervised scene flow learning in point clouds has attracted increasing attention. In the self-supervised manner, establishin…
HCRF-Flow: Scene Flow from Point Clouds with Continuous High-order CRFs and Position-aware Flow Embedding
Ruibo Li, Guosheng Lin, Tong He +2
Scene flow in 3D point clouds plays an important role in understanding dynamic environments. Although significant advances have been made by deep neural networks, the performance i…