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Feb 23, 2025 · Video-R1 significantly outperforms previous models across most benchmarks. Notably, on VSI-Bench, which focuses on spatial reasoning in videos, Video-R1-7B achieves a new state-of-the-art accuracy of 35.8%, surpassing GPT-4o, a proprietary model, while using only 32 frames and 7B parameters. This highlights the necessity of explicit reasoning capability in solving video tasks, and confirms the Jun 24, 2025 · OmniAvatar: Efficient Audio-Driven Avatar Video Generation with Adaptive Body Animation Qijun Gan · Ruizi Yang · Jianke Zhu · Shaofei Xue · Steven Hoi Zhejiang University, Alibaba Group Jan 21, 2025 · VideoLLaMA 3 is a series of multimodal foundation models with frontier image and video understanding capacity. 💡Click here to show detailed performance on video benchmarks Feb 15, 2025 · Solve Visual Understanding with Reinforced VLMs. Contribute to om-ai-lab/VLM-R1 development by creating an account on GitHub. Mar 17, 2025 · GCD: GCD synthesizes large-angle novel viewpoints of 4D dynamic scenes from a monocular video. ReCapture: a method for generating new videos with novel camera trajectories from a single user-provided video. Trajectory Attention: Trajectory Attention facilitates various tasks like camera motion control on images and videos, and video editing. yt-dlp is a feature-rich command-line audio/video downloader with support for thousands of sites. The project is a fork of youtube-dl based on the now inactive youtube-dlc. INSTALLATION Detailed instructions Release Files Update Dependencies Compile USAGE AND OPTIONS General Options Network Options Geo-restriction Video Selection Download Options Qwen2.5-VL is the multimodal large language model series developed by Qwen team, Alibaba Cloud. – QwenLM/Qwen2.5-VL [WIP] The all in one inference optimization solution for ComfyUI, universal, flexible, and fast. – chengzeyi/Comfy-WaveSpeed Grounded SAM 2 Video Object Tracking Demo with Custom Video Input (with Grounding DINO 1.5 & 1.6)

Users can upload their own video file (e.g. assets/hippopotamus.mp4) and specify their custom text prompts for grounding and tracking with Grounding DINO 1.5 and SAM 2 by using the following scripts: Pusa (pu: ‘sA:, from “Thousand-Hand Guanyin” in Chinese) introduces a paradigm shift in video diffusion modeling through frame-level noise control with vectorized timesteps, departing from conventional scalar timestep approaches.