33 citations · 43 across the 15 of their papers we have counts for
17 papers
Scaling Discovery Requires Forgetting
Shangyin Tan, Ben Zhou, Lakshya A Agrawal +8
Automated research with LLM agents has shown promising results in various domains. Through iterative refinement and scored verifiers, LLM agents are capable of discovering new idea…
InfiniSplat: Implicit Gaussian Decoding for Large-Baseline Monocular View Synthesis
Jiawei Wang, Hao Yu, Yongzhen Hu +7
Single-image feed-forward 3D Gaussian Splatting (3DGS) aims to directly generate a renderable 3D scene representation from one input image, avoiding the cost of multi-view capture…
AutoPath: Learning Transferable Goal-Conditioned Stochastic Path Prior for Safe Navigation Without Human Demonstrations
Ziyang Zhang, Boyang Zhou, Zesong Yang +8
Real-time navigation in cluttered and dynamic environments requires collision-free and dynamically feasible motion under limited perception. However, feasible navigation behaviors…
Habitat-GS: A High-Fidelity Navigation Simulator with Dynamic Gaussian Splatting
Ziyuan Xia, Jingyi Xu, Chong Cui +9
Training embodied AI agents depends critically on the visual fidelity of simulation environments and the ability to model dynamic humans. Current simulators predominantly rely on m…
PhysSkin: Real-Time and Generalizable Physics-Based Animation via Self-Supervised Neural Skinning
Yuanhang Lei, Tao Cheng, Xingxuan Li +6
Achieving real-time physics-based animation that generalizes across diverse 3D shapes and discretizations remains a fundamental challenge. We introduce PhysSkin, a physics-informed…
DiffWind: Physics-Informed Differentiable Modeling of Wind-Driven Object Dynamics
Yuanhang Lei, Boming Zhao, Zesong Yang +10
Modeling wind-driven object dynamics from video observations is highly challenging due to the invisibility and spatio-temporal variability of wind, as well as the complex deformati…
InfiniDepth: Arbitrary-Resolution and Fine-Grained Depth Estimation with Neural Implicit Fields
Hao Yu, Haotong Lin, Jiawei Wang +7
Existing depth estimation methods are fundamentally limited to predicting depth on discrete image grids. Such representations restrict their scalability to arbitrary output resolut…
Precise Action-to-Video Generation Through Visual Action Prompts
Yuang Wang, Chao Wen, Haoyu Guo +5
We present visual action prompts, a unified action representation for action-to-video generation of complex high-DoF interactions while maintaining transferable visual dynamics acr…
Towards Depth Foundation Model: Recent Trends in Vision-Based Depth Estimation
Zhen Xu, Hongyu Zhou, Sida Peng +13
Depth estimation is a fundamental task in 3D computer vision, crucial for applications such as 3D reconstruction, free-viewpoint rendering, robotics, autonomous driving, and AR/VR…
Glossy Object Reconstruction with Cost-effective Polarized Acquisition
Bojian Wu, Yifan Peng, Ruizhen Hu +1
The challenge of image-based 3D reconstruction for glossy objects lies in separating diffuse and specular components on glossy surfaces from captured images, a task complicated by…
BoxDreamer: Dreaming Box Corners for Generalizable Object Pose Estimation
Yuanhong Yu, Xingyi He, Chen Zhao +7
This paper presents a generalizable RGB-based approach for object pose estimation, specifically designed to address challenges in sparse-view settings. While existing methods can e…
Ready-to-React: Online Reaction Policy for Two-Character Interaction Generation
Zhi Cen, Huaijin Pi, Sida Peng +5
This paper addresses the task of generating two-character online interactions. Previously, two main settings existed for two-character interaction generation: (1) generating one's…
Motion-2-To-3: Leveraging 2D Motion Data for 3D Motion Generations
Ruoxi Guo, Huaijin Pi, Zehong Shen +8
Text-driven human motion synthesis has showcased its potential for revolutionizing motion design in the movie and game industry. Existing methods often rely on 3D motion capture da…
PC-Planner: Physics-Constrained Self-Supervised Learning for Robust Neural Motion Planning with Shape-Aware Distance Function
Xujie Shen, Haocheng Peng, Zesong Yang +4
Motion Planning (MP) is a critical challenge in robotics, especially pertinent with the burgeoning interest in embodied artificial intelligence. Traditional MP methods often strugg…
World-Grounded Human Motion Recovery via Gravity-View Coordinates
Zehong Shen, Huaijin Pi, Yan Xia +6
We present a novel method for recovering world-grounded human motion from monocular video. The main challenge lies in the ambiguity of defining the world coordinate system, which v…
MaPa: Text-driven Photorealistic Material Painting for 3D Shapes
Shangzhan Zhang, Shangzan Zhang, Sida Peng +8
This paper aims to generate materials for 3D meshes from text descriptions. Unlike existing methods that synthesize texture maps, we propose to generate segment-wise procedural mat…
SAM-guided Graph Cut for 3D Instance Segmentation
Haoyu Guo, He Zhu, Sida Peng +4
This paper addresses the challenge of 3D instance segmentation by simultaneously leveraging 3D geometric and multi-view image information. Many previous works have applied deep lea…