294 citations · 341 across the 22 of their papers we have counts for
22 papers
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…
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…
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…
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…
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…
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…
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…
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…
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{…
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…
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…
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…
AstraNav-Memory: Contexts Compression for Long Memory
Botao Ren, Junjun Hu, Xinda Xue +5
Lifelong embodied navigation requires agents to accumulate, retain, and exploit spatial-semantic experience across tasks, enabling efficient exploration in novel environments and r…
Taming Preference Mode Collapse via Directional Decoupling Alignment in Diffusion Reinforcement Learning
Chubin Chen, Sujie Hu, Jiashu Zhu +8
Recent studies have demonstrated significant progress in aligning text-to-image diffusion models with human preference via Reinforcement Learning from Human Feedback. However, whil…
AstraNav-World: World Model for Foresight Control and Consistency
Jintao Chen, Junjun Hu, Haochen Bai +11
Embodied navigation in open, dynamic environments demands accurate foresight of how the world will evolve and how actions will unfold over time. We propose AstraNav-World, an end-t…
Instance-Aware Robust Consistency Regularization for Semi-Supervised Nuclei Instance Segmentation
Zenan Lin, Wei Li, Jintao Chen +5
Nuclei instance segmentation in pathological images is crucial for downstream tasks such as tumor microenvironment analysis. However, the high cost and scarcity of annotated data l…
OmniNav: A Unified Framework for Prospective Exploration and Visual-Language Navigation
Xinda Xue, Junjun Hu, Minghua Luo +7
Embodied navigation presents a core challenge for intelligent robots, requiring the comprehension of visual environments, natural language instructions, and autonomous exploration.…
Omni-Effects: Unified and Spatially-Controllable Visual Effects Generation
Fangyuan Mao, Aiming Hao, Jintao Chen +7
Visual effects (VFX) are essential visual enhancements fundamental to modern cinematic production. Although video generation models offer cost-efficient solutions for VFX productio…
UniEdit-I: Training-free Image Editing for Unified VLM via Iterative Understanding, Editing and Verifying
Chengyu Bai, Jintao Chen, Xiang Bai +4
While Unified Vision-Language Models promise to synergistically combine the high-level semantic understanding of vision-language models with the generative fidelity of diffusion mo…
FastInit: Fast Noise Initialization for Temporally Consistent Video Generation
Chengyu Bai, Yuming Li, Zhongyu Zhao +5
Video generation has made significant strides with the development of diffusion models; however, achieving high temporal consistency remains a challenging task. Recently, FreeInit…
SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation
Jun Xia, Lirong Wu, Jintao Chen +2
Graph contrastive learning (GCL) has emerged as a dominant technique for graph representation learning which maximizes the mutual information between paired graph augmentations tha…
ProGCL: Rethinking Hard Negative Mining in Graph Contrastive Learning
Jun Xia, Lirong Wu, Ge Wang +2
Contrastive Learning (CL) has emerged as a dominant technique for unsupervised representation learning which embeds augmented versions of the anchor close to each other (positive s…