collaborators

12 papers

cs.CV2026

Uncertainty DMD: Restoring Diversity in Few-Step Autoregressive Video Distillation

Zixuan Duan, Xunzhi Xiang, Yabo Chen +6

Few-step distillation improves the efficiency of autoregressive (AR) video generation, but often causes diversity collapse: under the same prompt, different noise samples tend to p…

cs.CV2026

Search-to-World: Evaluation of 3D World Delivery from User Request through Web Search

Zixiao Gu, Yabo Chen, Xunzhi Xiang +5

Agentic systems can interpret user requests, search the live web, and use external tools, but their ability to transform retrieved web content into a usable 3D world has not been s…

cs.CV2026

PhyEditBench: A Real-World Multi-Stage Benchmark for Physics-Aware Image Editing

Shengbin Guo, Shaokang He, Chaoyue Meng +4

While instruction-based image editing, enabled by multi-modal generative models, has advanced significantly, existing benchmarks lack a comprehensive evaluation of physics-based re…

cs.CV2026

VideoWeave: Unlocking Geometric Consistency in Video Generation via Joint Geometry-Video Modeling

Xunzhi Xiang, Zixuan Duan, Yabo Chen +8

Large-scale video diffusion models often fail to preserve 3D structure over time, causing geometric drift and implausible motion under viewpoint changes. Existing methods usually e…

cs.CV2026

Geometry-Aware Implicit Memory for Video World Models

Zhengxuan Wei, Xu Guo, Xinghui Li +8

Video world models aim to simulate controllable visual environments, but long-horizon rollouts depend on what the model remembers after observations leave its native context window…

cs.CV2026

Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation

Junyuan Ma, Xunzhi Xiang, Wenbin Li +2

Vision foundation models (VFMs) have achieved strong performance across various vision tasks. However, it still remains challenging to apply VFMs for cross-domain few-shot segmenta…

cs.CV2026

SymphoMotion: Joint Control of Camera Motion and Object Dynamics for Coherent Video Generation

Guiyu Zhang, Yabo Chen, Xunzhi Xiang +3

Controlling both camera motion and object dynamics is essential for coherent and expressive video generation, yet current methods typically handle only one motion type or rely on a…

cs.CV2026

Pathwise Test-Time Correction for Autoregressive Long Video Generation

Xunzhi Xiang, Zixuan Duan, Guiyu Zhang +7

Distilled autoregressive diffusion models facilitate real-time short video synthesis but suffer from severe error accumulation during long-sequence generation. While existing Test-…

cs.CV2025

Denoising Vision Transformer Autoencoder with Spectral Self-Regularization

Xunzhi Xiang, Xingye Tian, Guiyu Zhang +5

Variational autoencoders (VAEs) typically encode images into a compact latent space, reducing computational cost but introducing an optimization dilemma: a higher-dimensional laten…

cs.CV2025

Macro-from-Micro Planning for High-Quality and Parallelized Autoregressive Long Video Generation

Xunzhi Xiang, Yabo Chen, Guiyu Zhang +10

Current autoregressive diffusion models excel at video generation but are generally limited to short temporal durations. Our theoretical analysis indicates that the autoregressive…

cs.CV2025

Make It Efficient: Dynamic Sparse Attention for Autoregressive Image Generation

Xunzhi Xiang, Qi Fan

Autoregressive conditional image generation models have emerged as a dominant paradigm in text-to-image synthesis. These methods typically convert images into one-dimensional token…

cs.CV2025

Proteus-ID: ID-Consistent and Motion-Coherent Video Customization

Guiyu Zhang, Chen Shi, Zijian Jiang +4

Video identity customization seeks to synthesize realistic, temporally coherent videos of a specific subject, given a single reference image and a text prompt. This task presents t…