A Motion-Aware Frame Sampling (MAFS) strategy for constructing motion-aware captions enables more effective utilization of video frames and improves captioning quality for large-motion videos.
Recent advances in generative video models have enabled camera-controlled world video generation, allowing models to synthesize videos under user-defined camera trajectories. However, existing video quality assessment (VQA) methods are mainly developed for natural videos and fail to capture the unique perceptual charac...
VideoVIBE is introduced, a video-grounded benchmark that transforms human-operated webpage recordings into fine-grained diagnostic tasks and V2Lens is proposed, a training-free, evidence-grounded multi-agent system that challenges and selectively refines initial video-based diagnoses through targeted visual and source-...
ClearText-Video (CTVid), a large-scale, scene-text-aware benchmark for studying text-centric video understanding under controlled quality variation, is introduced, providing a rigorous foundation for restoration-aware, quality-robust text-centric video systems.
This work extends the previous dataset HVEval with pairwise preference annotations and proposes MoE-Rater, a Mixture-of-Experts (MoE)-inspired and multimodal large language model (MLLM)-based all-in-one method that supports multi-dimensional quality rating, multi-dimensional pairwise comparison, and category-specific q...
An AI-based Text-to-Video Generation framework that converts natural language prompts into short animated video sequences using diffusion and deep learning techniques is presented, providing a lightweight and costeffective platform for video generation.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
