5 citations · 13 across the 16 of their papers we have counts for
17 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…
EditVerse3D: High-Quality 3D Object Editing with Region-Aware Learning
Youtan Yin, Yanning Zhou, Jiacheng Wei +6
Local editing of 3D objects remains a long-standing challenge. When interacting with 3D content, humans naturally tend to specify a coarse region of interest for modification rathe…
Claw AI Lab: An Autonomous Multi-Agent Research Team
Fan Wu, Cheng Chen, Zhenshan Tan +12
We present Claw AI Lab, a lab-native autonomous research platform that advances automated research from a hidden prompt-to-paper pipeline into an interactive AI laboratory. Rather…
PhyCustom: Towards Realistic Physical Customization in Text-to-Image Generation
Fan Wu, Cheng Chen, Zhoujie Fu +4
Recent diffusion-based text-to-image customization methods have achieved significant success in understanding concrete concepts to control generation processes, such as styles and…
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…
Prim2Room: Layout-Controllable Room Mesh Generation from Primitives
Chengzeng Feng, Jiacheng Wei, Cheng Chen +5
We propose Prim2Room, a novel framework for controllable room mesh generation leveraging 2D layout conditions and 3D primitive retrieval to facilitate precise 3D layout specificati…
OrientDream: Streamlining Text-to-3D Generation with Explicit Orientation Control
Yuzhong Huang, Zhong Li, Zhang Chen +4
In the evolving landscape of text-to-3D technology, Dreamfusion has showcased its proficiency by utilizing Score Distillation Sampling (SDS) to optimize implicit representations su…
Text-to-Image Rectified Flow as Plug-and-Play Priors
Xiaofeng Yang, Cheng Chen, Xulei Yang +2
Large-scale diffusion models have achieved remarkable performance in generative tasks. Beyond their initial training applications, these models have proven their ability to functio…
Sync4D: Video Guided Controllable Dynamics for Physics-Based 4D Generation
Zhoujie Fu, Jiacheng Wei, Wenhao Shen +5
In this work, we introduce a novel approach for creating controllable dynamics in 3D-generated Gaussians using casually captured reference videos. Our method transfers the motion o…
REACTO: Reconstructing Articulated Objects from a Single Video
Chaoyue Song, Jiacheng Wei, Chuan-Sheng Foo +2
In this paper, we address the challenge of reconstructing general articulated 3D objects from a single video. Existing works employing dynamic neural radiance fields have advanced…
Fine Structure-Aware Sampling: A New Sampling Training Scheme for Pixel-Aligned Implicit Models in Single-View Human Reconstruction
Kennard Yanting Chan, Fayao Liu, Guosheng Lin +2
Pixel-aligned implicit models, such as PIFu, PIFuHD, and ICON, are used for single-view clothed human reconstruction. These models need to be trained using a sampling training sche…
Learn to Optimize Denoising Scores for 3D Generation: A Unified and Improved Diffusion Prior on NeRF and 3D Gaussian Splatting
Xiaofeng Yang, Yiwen Chen, Cheng Chen +5
We propose a unified framework aimed at enhancing the diffusion priors for 3D generation tasks. Despite the critical importance of these tasks, existing methodologies often struggl…
Few-shot Image Generation via Style Adaptation and Content Preservation
Xiaosheng He, Fan Yang, Fayao Liu +1
Training a generative model with limited data (e.g., 10) is a very challenging task. Many works propose to fine-tune a pre-trained GAN model. However, this can easily result in ove…
Learning-Based Biharmonic Augmentation for Point Cloud Classification
Jiacheng Wei, Guosheng Lin, Henghui Ding +2
Point cloud datasets often suffer from inadequate sample sizes in comparison to image datasets, making data augmentation challenging. While traditional methods, like rigid transfor…
Leveraging Large-Scale Pretrained Vision Foundation Models for Label-Efficient 3D Point Cloud Segmentation
Shichao Dong, Fayao Liu, Guosheng Lin
Recently, large-scale pre-trained models such as Segment-Anything Model (SAM) and Contrastive Language-Image Pre-training (CLIP) have demonstrated remarkable success and revolution…
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…
Learning Spatial and Temporal Variations for 4D Point Cloud Segmentation
Shi Hanyu, Wei Jiacheng, Wang Hao +2
LiDAR-based 3D scene perception is a fundamental and important task for autonomous driving. Most state-of-the-art methods on LiDAR-based 3D recognition tasks focus on single frame…