activity
20212026
most citedSimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation

294 citations · 341 across the 22 of their papers we have counts for

collaborators
Showing 2026Show all

12 papers · 1 filter

cs.CV2026

Vidu S2: Real-Time Interactive, Editable, and Spatial Video Generation

Jintao Zhang, Kai Jiang, Jintao Chen +32

We present Vidu S2, which comprises Vidu S2-Avatar, a real-time interactive digital-character model, and Vidu S2-Editing, a real-time video editing model. Moreover, we explore the…

cs.CV2026

Learning Explicit Physical Parameter Control and Benchmarking for Video Generation

Yanxun Li, Hao Wen, Bingze Song +7

Recent advances in image-to-video generation have improved visual realism, making physically grounded and controllable dynamics an important step toward future world simulation. Cu…

cs.CV2026

Vidu S1: A Real-Time Interactive Video Generation Model

Jintao Zhang, Kai Jiang, Jintao Chen +24

We introduce Vidu S1, a real-time interactive video generation model supporting voice control of digital characters. Users can control video generation content at any moment throug…

cs.RO2026

IOI: Decoupling Kinematics and Physics for Interactive World Models

Chengyu Bai, Peidong Jia, Tiecheng Guo +11

Developing generalist embodied agents requires interactive environments providing visually realistic feedback and accurate action-conditioned dynamics. Interactive world models add…

cs.RO2026

MV-WAM: Manifold-Aware World Action Model with Value Augmentation

Jintao Chen, Peidong Jia, Qingpo Wuwu +13

Achieving robust and generalizable manipulation across diverse environments remains a fundamental challenge in embodied robotics. Recent world action models achieve strong in-domai…

cs.RO2026

VEGA: Visual Encoder Grounding Alignment for Spatially-Aware Vision-Language-Action Models

Hao Wang, Xiaobao Wei, Jingyang He +10

Precise spatial reasoning is fundamental to robotic manipulation, yet the visual backbones of current vision-language-action (VLA) models are predominantly pretrained on 2D image d…

cs.AI2026

Intern-Atlas: A Methodological Evolution Graph as Research Infrastructure for AI Scientists

Yujun Wu, Dongxu Zhang, Xinchen Li +11

Existing research infrastructure is fundamentally document-centric, providing citation links between papers but lacking explicit representations of methodological evolution. In par…

cs.SE2026

Programming with Data: Test-Driven Data Engineering for Self-Improving LLMs from Raw Corpora

Chenkai Pan, Xinglong Xu, Yuhang Xu +6

Reliably transferring specialized human knowledge from text into large language models remains a fundamental challenge in artificial intelligence. Fine-tuning on domain corpora has…

cs.CV2026

AnchorSeg: Language Grounded Query Banks for Reasoning Segmentation

Rui Qian, Chuanhang Deng, Qiang Huang +6

Reasoning segmentation requires models to ground complex, implicit textual queries into precise pixel-level masks. Existing approaches rely on a single segmentation token $\texttt{…

cs.CV2026

Grounded Forcing: Bridging Time-Independent Semantics and Proximal Dynamics in Autoregressive Video Synthesis

Jintao Chen, Chengyu Bai, Junjun Hu +2

Autoregressive video synthesis offers a promising pathway for infinite-horizon generation but is fundamentally hindered by three intertwined challenges: semantic forgetting from co…

cs.CV2026

ConceptWeaver: Weaving Disentangled Concepts with Flow

Jintao Chen, Aiming Hao, Xiaoqing Chen +6

Pre-trained flow-based models excel at synthesizing complex scenes yet lack a direct mechanism for disentangling and customizing their underlying concepts from one-shot real-world…

cs.AI2026

The Trinity of Consistency as a Defining Principle for General World Models

Jingxuan Wei, Siyuan Li, Yuhang Xu +21

The construction of World Models capable of learning, simulating, and reasoning about objective physical laws constitutes a foundational challenge in the pursuit of Artificial Gene…