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AI video

AI video generation and understanding: text-to-video models, editing tools and how film and video making change.

8 picksLast 30 days 4 itemsCollected 41 items

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Picks about AI video

10/16Fri
  1. 雷锋网82

    Kling 4.0 launches with 30-second native video generation and 10-bit HDR

    Kling AI launched Kling 4.0 on October 15, introducing native video generation up to 30 seconds, 10-bit HDR output, and claimed improvements in fast motion and complex camera movements. The model supports up to 15 combined reference assets, up to 10 keyframe inputs, and prompts up to 8000 tokens. Support for 4K, 1080p, and 21:9 output, repeated video extensions totaling up to 2 minutes, and API access are described as forthcoming.

    Why it matters: The reference inputs, keyframe controls, and HDR output provide concrete context for assessing how Kling 4.0 could fit into professional video production workflows.

10/15Thu
  1. 雷锋网83

    Kunlun Tech releases and open-sources Matrix-Game 4.0 for single-GPU real-time gameplay

    Kunlun Tech released Matrix-Game 4.0, reporting 480p gameplay at 60 FPS on a single NVIDIA RTX 5090 with end-to-end response latency below 150 milliseconds. Its game engine maintains world state and interaction rules, a coding agent modifies game logic, and a video model generates detailed visuals from structural frames and dynamic semantic conditions. Code, weights, and simplified scene models are available at https://huggingface.co/Skywork/Matrix-Game-4.0.

    Why it matters: The architecture offers a transferable approach to interactive generation by assigning persistent state and rules to an engine while using a video model for visual rendering.

9/25Fri
  1. Google Research65

    Google Research introduces AI video co-director framework for coherent long-form video generation

    Google Research has introduced an AI video co-director multi-agent framework, an orchestration layer on top of Gemini and Veo. It models long-form video generation as a problem of global optimization and world-state tracking to mitigate semantic drift and cascading failures.

    Why it matters: Google breaks long-video generation into four composable frameworks and reports continuity improvements across several benchmarks, illustrating how multi-agent orchestration can reduce semantic drift.

  2. Google DeepMind71

    Google DeepMind releases Gemini 3.8 Live with Live Avatar

    Google DeepMind has released Gemini 3.8 Live with Live Avatar, combining real-time video generation and voice to give Gemini's conversational model a near-real-time visual avatar, with accurate lip-sync, natural expressions, and smooth turn-taking.

    Why it matters: Gemini 3.8 Live adds real-time video avatars, giving readers a view of how multimodal conversation is being applied in enterprise settings and where the feature is available.

9/2Wed
  1. Google DeepMind74

    Google DeepMind adds agentic video understanding to Gemini 3.7 Flash and other models

    Google DeepMind has introduced agentic video understanding for Gemini 3.7 Flash, 3.6 Flash, and 3.5 Flash-Lite. Video analysis uses up to 88% fewer tokens and costs up to 66% less, while accuracy improves by up to 7%.

    Why it matters: The article gives token and cost reductions and access details, helping readers assess how the economics of long-video analysis are changing.

8/28Fri
  1. Google DeepMind68

    Google DeepMind releases Gemini Omni 1.1 Flash

    Google DeepMind has released Gemini Omni 1.1 Flash, offering developers video-generation controls through the Gemini API in Google AI Studio.

    Why it matters: The article details capabilities such as extending scenes, interpolating between first and last frames, and 4K upscaling, helping readers assess how controllable video-generation workflows are becoming.

7/1Wed
5/18Mon
  1. Google DeepMind85

    Google DeepMind releases Gemini Omni Flash for video generation from multimodal inputs

    Google DeepMind has released Gemini Omni Flash, the first model in the Omni family. It can combine images, audio, video, and text as inputs to generate high-quality video and supports iterative video editing through natural-language conversation.

    Why it matters: The source describes the first Omni model's input and output capabilities and where it is available, helping readers assess the practical reach of multimodal video generation.