Video large multimodal models have improved at identifying cross-frame visual evidence and conducting open-web retrieval, as reflected in recent video deep research (VDR) benchmarks. However, existing VDR benchmarks primarily evaluate tool-use trajectories and final responses without verifying whether a model actually...
Xi-Yuan Liang, Yu Wang, Zu-Hao Yang et al.· 0 citations
VideoX-Qwen, an integrated data-construction and model-training framework for general instruction-based video editing, and a unified Qwen-Wan editor that combines multimodal semantic conditioning with dense source-video latent guidance provide a practical foundation for more capable instruction-driven video editing.
J.Jenny Li, Di Shao, Xin-Yu Chen et al.· 0 citations
InstructVVT is proposed, an instruction-driven and reference-guided video virtual try-on framework based on a Diffusion Transformer that operates without inference-time spatial priors that outperforms state-of-the-art open-source methods in garment fidelity, structural preservation, and temporal consistency, despite re...
Di Shao, Song-Han Wu, Xin-Yu Chen et al.· 0 citations
Evaluation of state-of-the-art open-source and closed-source MLLMs reveals that current MLLMs function as lossy summarizers rather than faithful memorizers, highlighting the need for architectures with genuine long-term spatiotemporal memory.
