activity
20242026
collaborators

9 papers

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

Motion-as-Prompt: Enhancing Motion Reasoning in Multimodal Large Language Models via Motion-Guided Cross-Frame Visual Prompting

Xikai Sun, Kebin Liu, Haotian Wang +3

Motion-centric video reasoning is fundamental to interactive applications such as robotic manipulation and autonomous navigation. However, multimodal large language models (MLLMs)…

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.CV2026

Response-G1: Explicit Scene Graph Modeling for Proactive Streaming Video Understanding

Ke Ma, Jiaqi Tang, Bin Guo +8

Proactive streaming video understanding requires Video-LLMs to decide when to respond as a video unfolds, a task where existing methods often fall short due to their implicit, quer…

cs.CV2026

VAR RL Done Right: Tackling Asynchronous Policy Conflicts in Visual Autoregressive Generation

Shikun Sun, Liao Qu, Huichao Zhang +8

Visual generation is dominated by three paradigms: AutoRegressive (AR), diffusion, and Visual AutoRegressive (VAR) models. Unlike AR and diffusion, VARs operate on heterogeneous in…

cs.CV2026

NextFlow: Unified Sequential Modeling Activates Multimodal Understanding and Generation

Huichao Zhang, Liao Qu, Yiheng Liu +33

We present NextFlow, a unified decoder-only autoregressive transformer trained on 6 trillion interleaved text-image discrete tokens. By leveraging a unified vision representation w…

cs.CV2025

XVerse: Consistent Multi-Subject Control of Identity and Semantic Attributes via DiT Modulation

Bowen Chen, Mengyi Zhao, Haomiao Sun +4

Achieving fine-grained control over subject identity and semantic attributes (pose, style, lighting) in text-to-image generation, particularly for multiple subjects, often undermin…

cs.CV2025

DetailFlow: 1D Coarse-to-Fine Autoregressive Image Generation via Next-Detail Prediction

Yiheng Liu, Liao Qu, Huichao Zhang +10

This paper presents DetailFlow, a coarse-to-fine 1D autoregressive (AR) image generation method that models images through a novel next-detail prediction strategy. By learning a re…

cs.CV2024

TokenFlow: Unified Image Tokenizer for Multimodal Understanding and Generation

Liao Qu, Huichao Zhang, Yiheng Liu +7

We present TokenFlow, a novel unified image tokenizer that bridges the long-standing gap between multimodal understanding and generation. Prior research attempt to employ a single…