61 citations · 350 across the 65 of their papers we have counts for
63 papers · 1 filter
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…
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…
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…
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…
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…
FLARE: Feed-forward Geometry, Appearance and Camera Estimation from Uncalibrated Sparse Views
Shangzhan Zhang, Jianyuan Wang, Yinghao Xu +5
We present FLARE, a feed-forward model designed to infer high-quality camera poses and 3D geometry from uncalibrated sparse-view images (i.e., as few as 2-8 inputs), which is a cha…
Edicho: Consistent Image Editing in the Wild
Qingyan Bai, Hao Ouyang, Yinghao Xu +5
As a verified need, consistent editing across in-the-wild images remains a technical challenge arising from various unmanageable factors, like object poses, lighting conditions, an…
Representing Long Volumetric Video with Temporal Gaussian Hierarchy
Zhen Xu, Yinghao Xu, Zhiyuan Yu +4
This paper aims to address the challenge of reconstructing long volumetric videos from multi-view RGB videos. Recent dynamic view synthesis methods leverage powerful 4D representat…
3DitScene: Editing Any Scene via Language-guided Disentangled Gaussian Splatting
Qihang Zhang, Yinghao Xu, Chaoyang Wang +4
Scene image editing is crucial for entertainment, photography, and advertising design. Existing methods solely focus on either 2D individual object or 3D global scene editing. This…
Collaborative Video Diffusion: Consistent Multi-video Generation with Camera Control
Zhengfei Kuang, Shengqu Cai, Hao He +4
Research on video generation has recently made tremendous progress, enabling high-quality videos to be generated from text prompts or images. Adding control to the video generation…
GRM: Large Gaussian Reconstruction Model for Efficient 3D Reconstruction and Generation
Yinghao Xu, Zifan Shi, Wang Yifan +5
We introduce GRM, a large-scale reconstructor capable of recovering a 3D asset from sparse-view images in around 0.1s. GRM is a feed-forward transformer-based model that efficientl…