{"podcast":{"title":"Daily Paper Cast","slug":"daily-paper-cast-7079649","podcast_index_feed_id":7079649,"rss_url":"https://feeds.transistor.fm/daily-paper-cast-ai","website_url":"https://dailypapercast.transistor.fm/","image_url":"https://img.transistorcdn.com/IxaBeiMluxrMS9W9wB8hFMfmvH27KvwaSMzuhucupn0/rs:fill:0:0:1/w:1400/h:1400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS81Zjg1/YzRhODczMDU4MmE4/OGMwN2FiNDlmYzI2/MDliMi5qcGVn.jpg","author":"Jingwen Liang, Gengyu Wang","episode_count":2000,"summary":"We update every weekday to discuss highest-voted papers from Huggingface Daily Paper (https://huggingface.co/papers). Both the podcast scripts and audio are generated by AI. Feedback and suggestions are welcome! Email us: dailypapercast.ai@gmail.com Creator: Jingwen Liang, 3D ML, https://www.linkedin.com/in/jingwen-liang/ Gengyu Wang, LLM ML, http://wanggengyu.com Listen on: Spotify: https://open.spotify.com/show/21nrhmdaA8qoBiH8q03NXL Apple Podcast: https://podcasts.apple.com/us/podcast/daily-paper-cast/id1777620236 Cover Image by Kawen Kuang https://kawen.art","last_synced_at":"2026-09-09T20:18:08.781137+00:00","page_url":"https://stenobird.com/podcast/daily-paper-cast-7079649"},"episode":{"title":"LiveEdit: Towards Real-Time Diffusion-Based Streaming Video Editing","slug":"liveedit-towards-real-time-diffusion-based-streaming-video-editing","published_at":"2026-07-01T04:26:00+00:00","page_url":"https://stenobird.com/podcast/daily-paper-cast-7079649/liveedit-towards-real-time-diffusion-based-streaming-video-editing","show_page_url":"https://stenobird.com/podcast/daily-paper-cast-7079649","url":"https://share.transistor.fm/s/f9ed41cb","audio_url":"https://media.transistor.fm/f9ed41cb/70405b84.mp3","summary":"🤗 Upvotes: 72 | cs.CV Authors: Xinyu Wang, Chongbo Zhao, Fangneng Zhan, Yue Ma Title: LiveEdit: Towards Real-Time Diffusion-Based Streaming Video Editing Arxiv: http://arxiv.org/abs/2606.26740v1 Abstract: Streaming video editing has made rapid progress, yet practical deployment is still limited by two core issues: maintaining stable backgrounds and non-edited regions over time, and achieving the low latency required for real-time interactive scenarios. Meanwhile, recent streaming video generation methods are mostly developed for synthesis and cannot be directly applied to editing due to the strict preservation requirement and region-specific control. In this work, we present a novel streaming video editing framework that performs causal, frame-by-frame editing with strong content preservation and real-time responsiveness. Our key design is a three-stage distillation pipeline that progressively transfers editing capability from a powerful bidirectional foundation model to an efficient unidirectional streaming editor, enabling stable long-horizon edits without sacrificing visual fidelity. To further support real-time deployment, we introduce an AR-oriented mask cache that reuses region-related computation across frames, substantially reducing redundant processing and accelerating inference. Finally, we establish a dedicated benchmark for streaming video editing. Extensive evaluations demonstrate that our method achieves state-of-the-art visual quality among streaming baselines while drastically boosting inference speed to 12.66 FPS, making it suitable for interactive and augmented reality applications.","meta_description":"🤗 Upvotes: 72 | cs.CV Authors: Xinyu Wang, Chongbo Zhao, Fangneng Zhan, Yue Ma Title: LiveEdit: Towards Real-Time Diffusion-Based Streaming Video Editing…","key_points":[],"chapters":[],"topics":[],"duration_seconds":1398,"processing_state":"not_requested","actions":[{"name":"request_transcript","method":"POST","url":"https://stenobird.com/v1/public/podcasts/daily-paper-cast-7079649/episodes/liveedit-towards-real-time-diffusion-based-streaming-video-editing/transcription-requests","description":"Idempotently request low-priority transcript generation for this episode."},{"name":"read_markdown","method":"GET","url":"https://stenobird.com/podcast/daily-paper-cast-7079649/liveedit-towards-real-time-diffusion-based-streaming-video-editing.md","description":"Read the agent-friendly Markdown representation of this episode resource."}]}}