Bridging Video Understanding and Generation in a Unified Framework

Towards an Efficient and Unified Strategy for Video Understanding Applications

Understanding long-range videos remains a key challenge in computer vision due to high temporal redundancy and computational burden. Despite strong performance of recent models, they are constrained in terms of scalability and generalization when applied to longer video sequences. In this work, we present Keyframe-based Spatio-Temporal Adaptive Representation (K-STAR), a redundancy-aware video summarization framework designed to generate compact and semantically rich representations that are effective in downstream tasks. The proposed method jointly models appearance and motion cues while filtering redundant frames. Importantly, it preserves critical temporal transitions while significantly reducing the number of processed frames. Additionally, each key frame is encoded using object, scene, and background-aware prompts, enabling richer semantic representation. Evaluated on the UCF-101 dataset, K-STAR achieves Top-1 accuracy of 93.06% and Top-5 accuracy of $\mathbf{9 8. 7 3 \%}$, with $\mathbf{5 6} \times$ frame reduction and $\mathbf{1 1. 5} \times$ faster inference, demonstrating competitive performance with substantially improved efficiency.

Aura: Consistent Multi-Subject Video Generation via VLM-Grounded Semantic Alignment

A Aura, a unified framework for high-fidelity and identity-consistent video generation, and introduces AI director-level captions that provide dense and structured descriptions of video content to better capture scene dynamics and subject interactions.

Gen4U: Unifying Video Generation and Understanding via Diffusion

Gen4U (Generation for Understanding), a framework that repurposes these generative representations with a single forward pass, is introduced, achieving strong perception performance while fully preserving the model's ability to generate high-quality video.

Long-Horizon Video Generation with Temporally Consistent Diffusion and Scene-Graph Guidance

A novel framework that integrates temporally consistent diffusion models with dynamic scene-graph guidance that structurally constrains the generative process, ensuring that objects, their attributes, and their interrelationships remain stable over extended durations is introduced.

Streaming Diffusion Model for Fast Infrared and Visible Video Fusion

Multimodal Video Understanding: A Capability-Based Survey of Alignment, Expression, and Reasoning

Multimodal video understanding (MVU) has emerged as a fast-growing research frontier, driven by major advances in video-language pre-training and large multimodal models over the past decade. MVU aims to synergistically integrate visual, audio and textual modalities to interpret complex video semantics, supporting widespread downstream tasks including cross-modal retrieval, dense captioning, video question answering, event analysis and intelligent assistance. Despite the rapid proliferation of specialized MVU models, the community still lacks a unified capability-centric framework to systematically clarify the hierarchical competency architecture and evolutionary trajectory of state-of-the-art approaches. To address this issue, this paper presents a structured, comprehensive survey of the latest MVU progress, establishing a novel three-tier taxonomy that categorizes existing studies into cross-modal alignment, multi-granularity semantic expression and multimodal reasoning. Along this pipeline, we further systematically synthesize core modality fusion strategies, mainstream benchmark datasets and standardized evaluation protocols. Through a fine-grained analysis of representative published results, we highlight the critical impact of inconsistent evaluation settings, cross-experiment comparability bottlenecks and inherent methodological trade-offs between performance and efficiency. Finally, we identify and dissect three key open challenges: ultra-long video scalability, performance degradation from modality noise and missing data, and factual reliability risks in generative MVU systems. This capability-oriented systematic reference clarifies the methodological evolution logic of MVU, and provides actionable guidance for developing next-generation robust, high-performance multimodal video understanding systems.