collaborators

14 papers

cs.CV2026

FlexComposer: Unified Video Compositing from Images to Dynamic Footage with Flexible Trajectory Control

Songchun Zhang, Sitong Guo, Xianghao Kong +4

Generative video compositing, which involves inserting external assets seamlessly into existing video sequences, is essential for content creation and visual effects. However, exis…

cs.CV2026

MagicPrompt: Ultra-Lightweight Prompt Tuning for Video Generation

Yinhan Zhang, Dingwei Tan, Dinwei Tan +4

The paper introduces MagicPrompt, a lightweight method that uses attention-embedded soft prompts and dual-space reward feedback to fine‑tune large video diffusion models with less…

cs.CV2026

Video-MME-Logical: A Controlled Diagnostic Benchmark for Video Temporal-Logical Reasoning

Hohin Kwan, Hongyu Li, Ray Zhang +5

Recent interest in multimodal large language models (MLLMs) raises a central question: can they reason over dynamic visual evidence rather than merely recognize objects or events i…

cs.CV2026

EvalVerse: Pipeline-Aware and Expert-Calibrated Benchmarking for Professional Cinematic Video Generation

Songlin Yang, Haobin Zhong, Ruilin Zhang +23

The rapid evolution of generative video foundation models has propelled the field toward professional-grade cinematic synthesis. To achieve such demanding quality, the community tr…

cs.CV2026

UniVidX: A Unified Multimodal Framework for Versatile Video Generation via Diffusion Priors

Houyuan Chen, Hong Li, Xianghao Kong +8

Recent progress has shown that video diffusion models (VDMs) can be repurposed for diverse multimodal graphics tasks. However, existing methods often train separate models for each…

cs.CV2026

Pseudo-Unification: Entropy Probing Reveals Divergent Information Patterns in Unified Multimodal Models

Songlin Yang, Xianghao Kong, Anyi Rao

Unified multimodal models (UMMs) were designed to combine the reasoning ability of large language models (LLMs) with the generation capability of vision models. In practice, howeve…