Inside The AI Tools for Generating Video

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On today in tech history: 1998: Microsoft launched Windows 98, codenamed “Memphis,” succeeding Windows 95 with improved hardware support and Web Explorer 4.0 integration. This OS helped advance AI by providing a stable platform for tools like SNNS (Stuttgart Neural Network Simulator) and CLIPS (expert system framework), enabling researchers to develop early neural networks.

The AI Tools for Generating Video
Apple Finally Joins the AI Race
Deepmind’s Force Prompting
5 Recent AI Tools
Latest AI Research Papers

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Today’s trending AI news stories

The AI Tools for Generating Video: Between Concept and Creation

AI content tools are rapidly reshaping how images, video, and audio are created, edited, and monetized, shifting the main target from manual production to automated, scalable pipelines.

Topaz Labs’ Bloom takes on image resolution, offering an AI-powered upscaler that reinforces output by as much as 8× without sacrificing core details. Its five tuning modes, from Subtle to Max, give users granular control over stylization, generating 4 outputs per pass to speed up visual iteration. In parallel, Captions’ Mirage Studio introduces digital actors with micro expressive realism, rendered in minutes from text, audio, or images via an omni-modal foundation model built for fluid performance. ManusAI extends that vision with a scene-planning video engine that maps, designs, and animates entire narratives from a single prompt, bringing storyboard-to-screen automation to a brand new level.

Time Check!!! Only 4.5 hours left of free creative upscaling with the newly announced Bloom. Should you didn’t get the early access link yet, we have you: topazlabs.com/bloom

Share this post to spread the Bloom love!

— Topaz Labs (@topazlabs)
12:51 AM • Jun 3, 2025

Introducing Manus video generation.

Manus transforms your prompts into complete stories—structured, sequenced, and prepared to look at. With a single prompt, Manus plans each scene, crafts the visuals, and animates your vision. From storyboard creation to concept visualization—your

– Manusai (@manusai_hq)
2:51 pm • Jun 3, 2025

On the audio front, PlayAI’s PlayDiffusion replaces autoregressive editing with parallel speech inpainting, using a 20-step denoising loop to revive natural pacing and tone at lower computational cost. Rounding it out, Character.AI evolves chat into performance: animated avatars, real-time scene direction, and group streaming push beyond reactive dialogue toward scripted, persistent character arcs, soon joined by redesigned profiles and a social feed. Meanwhile, HeyGen’s AI Studio tightens control over avatar performance with tools like gesture mapping, speech mirroring, and voice-director modes, layering production polish onto a totally synthetic solid.

Apple reportedly tests AI models that match ChatGPT’s capabilities in internal benchmarks

Tim Cook, chief executive officer of Apple. Image: David Paul Morris/Bloomberg

Apple plans to open its small on-device AI models, reportedly with around 3 billion parameters, to third-party developers at WWDC 2025, enabling basic features similar to text summarization. These compact models run directly on Apple hardware but are far less capable than larger in-house models used internally.

Apple is internally testing cloud-based foundation models with 3, 7, 33, and 150 billion parameters; the most important matches ChatGPT’s performance in benchmarks, in accordance with employees. Yet these advanced systems remain confined to the corporate’s “Playground” testbed, with no timeline for public release. Insiders cite executive hesitancy over accuracy and hallucinations, leaving Apple to quietly license OpenAI’s chatbot as an alternative. Meanwhile, splashier projects, including a chatty recent Siri, AI-enhanced Shortcuts, and a health assistant dubbed “Mulberry,” have slipped to 2026 or later. Read more.

Deepmind’s “force prompting” lets AI create realistic video motion without physics engines

Researchers from DeepMind and Brown University introduced force prompting, a way that permits generative video models to simulate realistic motion using directional force inputs, without counting on physics engines or 3D modeling. Users define global or local forces as vector fields., wind across a scene or a faucet on a degree, which the system processes through a ControlNet-integrated Transformer model built on CogVideoX-5B-I2V.

The model found out that a full laundry basket moves slower than an empty one when pushed. | Image: Gillman et al.

The model, trained for at some point on 4 Nvidia A100 GPUs, uses synthetic datasets: 15,000 clips of wind-driven flags, 12,000 of rolling balls, and 11,000 of flower impacts. It generalizes well, preserving physical plausibility and elegance features, while learning intuitive concepts like mass. Despite outperforming text-based and motion-path baselines, and even PhysDreamer in realism and alignment with input forces, it still falters in complex scenarios. Force prompting offers a light-weight strategy to steer AI-generated motion through intuitive physical cues. Read more.