61 citations · 350 across the 65 of their papers we have counts for
71 papers
FOMI: A Force-Feedback Manipulation Interface for Teaching Contact-Rich Manipulation
Yishu Shen, Yiming Luo, Ruilin Wang +7
Robot-free collection with handheld interfaces enables scalable acquisition of human demonstrations for policy learning. However, existing handheld interfaces often provide limited…
Zero-WAM: In-Context World-Action Modeling from Human Videos for Open-Ended Task Generalization
Jiaming Zhou, Qihang Zhang, Gangwei Xu +9
Zero-shot cross-task generalization, where a policy must execute manipulation tasks never seen during training, remains a central challenge in robot learning. In large language mod…
4DAnyone: Create Anyone in 4D from a Casual Monocular Video
Yudong Jin, Tao Xie, Qihang Zhang +6
We present 4DAnyone, a framework for reconstructing 4D humans from an uncalibrated monocular video by generating reconstruction-grade multiview-consistent videos and lifting them i…
Thinking With Tools, Not With Pixels: Tool Calls as Text Scaffolds for Visual Reasoning
Jiahao Shao, Yuanbo Yang, Yiyi Liao +3
Tool-augmented vision-language models increasingly "think with images": they call crop, zoom, or code tools and reason over the returned pixels. However, recent work using blind te…
Native Video-Action Pretraining for Generalizable Robot Control
Qihang Zhang, Lin Li, Luyao Zhang +26
The advent of video-action models offers a promising path for robot control. Nevertheless, we argue that repurposing video generative models designed for digital content creation i…
Scaling Mixture-of-Experts Video Pretraining for Embodied Intelligence
Shuailei Ma, Jiaqi Liao, Xinyang Wang +24
Despite the recent promise in robot control, video generative models suffer from a domain mismatch due to their primary focus on content creation. For example, their design inheren…
Infinite Worlds with Versatile Interactions
Zelin Gao, Qiuyu Wang, Jiapeng Zhu +17
We present LingBot-World 2.0 (also known as LingBot-World-Infinity), an advanced iteration of LingBot-World featuring four distinct upgrades. (1) Our model achieves an unbounded in…
Image2Sim: Scaling Embodied Navigation via Generative Neural Simulator
Zihan Wang, Seungjun Lee, Yinghao Xu +1
Embodied navigation aims to build agents that interpret multimodal goals, reason in 3D space, and reach target destinations reliably in the real world. However, progress remains co…
Vision Pretraining for Dense Spatial Perception
Zelin Fu, Bin Tan, Changjiang Sun +6
Dense spatial perception is essential for physical intelligence, where visual systems are expected to recover structured, metric, and actionable representations from pixel observat…
RepWAM: World Action Modeling with Representation Visual-Action Tokenizers
Junke Wang, Qihang Zhang, Shuai Yang +5
This work presents RepWAM, a representation-centric world action model (WAM) built on representation visual-action tokenizers. Existing WAMs typically inherit reconstruction-orient…
Next Forcing: Causal World Modeling with Multi-Chunk Prediction
Gangwei Xu, Qihang Zhang, Jiaming Zhou +4
Autoregressive video generation has emerged as a powerful paradigm for World Action Models (WAMs). However, existing approaches suffer from slow training convergence and limited co…
GA-VLN: Geometry-Aware BEV Representation for Efficient Vision-Language Navigation
Jiahao Yang, Zihan Wang, Xiangyang Li +4
Despite significant progress in Vision-Language Navigation (VLN), existing approaches still rely on dense RGB videos that produce excessive patch tokens and lack explicit spatial s…
JODA: Composable Joint Dynamics for Articulated Objects
Tianhong Gao, Cheng Yu, Yinghao Xu +1
Articulated objects used in simulation and embodied AI are typically specified by geometry and kinematic structure, but lack the fine-grained dynamical effects that govern realisti…
SceneScribe-1M: A Large-Scale Video Dataset with Comprehensive Geometric and Semantic Annotations
Yunnan Wang, Kecheng Zheng, Jianyuan Wang +8
The convergence of 3D geometric perception and video synthesis has created an unprecedented demand for large-scale video data that is rich in both semantic and spatio-temporal info…
LingBot-Map: Geometric Context Transformer for Streaming 3D Reconstruction
Lin-Zhuo Chen, Jian Gao, Shangzhan Zhang +9
Streaming 3D reconstruction aims to recover 3D information, such as camera poses and point clouds, from a video stream, which necessitates geometric accuracy, temporal consistency,…
Advancing Open-source World Models
Robbyant Team, Zelin Gao, Qiuyu Wang +21
We present LingBot-World, an open-sourced world simulator stemming from video generation. Positioned as a top-tier world model, LingBot-World offers the following features. (1) It…
Masked Depth Modeling for Spatial Perception
Bin Tan, Changjiang Sun, Xiage Qin +8
Spatial visual perception is a fundamental requirement in physical-world applications like autonomous driving and robotic manipulation, driven by the need to interact with 3D envir…
Causal World Modeling for Robot Control
Lin Li, Qihang Zhang, Yiming Luo +9
This work highlights that video world modeling, alongside vision-language pre-training, establishes a fresh and independent foundation for robot learning. Intuitively, video world…
Scaling Instruction-Based Video Editing with a High-Quality Synthetic Dataset
Qingyan Bai, Qiuyu Wang, Hao Ouyang +10
Instruction-based video editing promises to democratize content creation, yet its progress is severely hampered by the scarcity of large-scale, high-quality training data. We intro…
Mixture of Contexts for Long Video Generation
Shengqu Cai, Ceyuan Yang, Lvmin Zhang +10
Long video generation is fundamentally a long context memory problem: models must retain and retrieve salient events across a long range without collapsing or drifting. However, sc…
Video World Models with Long-term Spatial Memory
Tong Wu, Shuai Yang, Ryan Po +4
Emerging world models autoregressively generate video frames in response to actions, such as camera movements and text prompts, among other control signals. Due to limited temporal…
Interspatial Attention for Efficient 4D Human Video Generation
Ruizhi Shao, Yinghao Xu, Yujun Shen +5
Generating photorealistic videos of digital humans in a controllable manner is crucial for a plethora of applications. Existing approaches either build on methods that employ templ…
GroomLight: Hybrid Inverse Rendering for Relightable Human Hair Appearance Modeling
Yang Zheng, Menglei Chai, Delio Vicini +5
We present GroomLight, a novel method for relightable hair appearance modeling from multi-view images. Existing hair capture methods struggle to balance photorealistic rendering wi…
CameraCtrl II: Dynamic Scene Exploration via Camera-controlled Video Diffusion Models
Hao He, Ceyuan Yang, Shanchuan Lin +7
This paper introduces CameraCtrl II, a framework that enables large-scale dynamic scene exploration through a camera-controlled video diffusion model. Previous camera-conditioned v…