Publications (11)
MeshMosaic: Scaling Artist Mesh Generation via Local-to-Global Assembly
Rui Xu, Tianyang Xue, Qiujie Dong +9
Scaling artist-designed meshes to high triangle numbers remains challenging for autoregressive generative models. Existing transformer-based methods suffer from long-sequence bottl…
Understanding What Affects the Generalization Gap in Visual Reinforcement Learning: Theory and Empirical Evidence
Jiafei Lyu, Le Wan, Xiu Li +1
Recently, there are many efforts attempting to learn useful policies for continuous control in visual reinforcement learning (RL). In this scenario, it is important to learn a gene…
FreqMark: Invisible Image Watermarking via Frequency Based Optimization in Latent Space
Yiyang Guo, Ruizhe Li, Mude Hui +5
Invisible watermarking is essential for safeguarding digital content, enabling copyright protection and content authentication. However, existing watermarking methods fall short in…
SEABO: A Simple Search-Based Method for Offline Imitation Learning
Jiafei Lyu, Xiaoteng Ma, Le Wan +3
Offline reinforcement learning (RL) has attracted much attention due to its ability in learning from static offline datasets and eliminating the need of interacting with the enviro…
Uncertainty-driven Trajectory Truncation for Data Augmentation in Offline Reinforcement Learning
Junjie Zhang, Jiafei Lyu, Xiaoteng Ma +4
Equipped with the trained environmental dynamics, model-based offline reinforcement learning (RL) algorithms can often successfully learn good policies from fixed-sized datasets, e…
Improving Fine-Grained Control via Aggregation of Multiple Diffusion Models
Conghan Yue, Zhengwei Peng, Shiyan Du +4
While many diffusion models perform well when controlling particular aspects such as style, character, and interaction, they struggle with fine-grained control due to dataset limit…
PartSAM: A Scalable Promptable Part Segmentation Model Trained on Native 3D Data
Zhe Zhu, Le Wan, Rui Xu +6
Segmenting 3D objects into parts is a long-standing challenge in computer vision. To overcome taxonomy constraints and generalize to unseen 3D objects, recent works turn to open-wo…
FlashMesh: Faster and Better Autoregressive Mesh Synthesis via Structured Speculation
Tingrui Shen, Yiheng Zhang, Chen Tang +6
Autoregressive models can generate high-quality 3D meshes by sequentially producing vertices and faces, but their token-by-token decoding results in slow inference, limiting practi…
QuadLink: Autoregressive Quad-Dominant Mesh Generation via Point-Relation Learning
Yiheng Zhang, Zhe Zhu, Tingrui Shen +11
The generation of production-ready quad-dominant meshes is a cornerstone of modern 3D content creation. Generating anisotropic quad-dominant meshes from point clouds is challenging…
Off-Policy RL Algorithms Can be Sample-Efficient for Continuous Control via Sample Multiple Reuse
Jiafei Lyu, Le Wan, Zongqing Lu +1
Sample efficiency is one of the most critical issues for online reinforcement learning (RL). Existing methods achieve higher sample efficiency by adopting model-based methods, Q-en…
State Advantage Weighting for Offline RL
Jiafei Lyu, Aicheng Gong, Le Wan +2
We present state advantage weighting for offline reinforcement learning (RL). In contrast to action advantage that we commonly adopt in QSA learning, we leverage state adv…