luokai

To clarify, I didn’t use any real human dance footage as reference for this video—everything was generated and then edited together. Each segment of my video is based on prompts that generally include the following elements:

luokai@luok.ai

The Seedance 2 model is incredibly powerful, completely overshadowing all other models. This is an original video I created in just one day, though the music was previously made using Suno.

This multimodal reference capability is quite rare among current AI video tools. In theory, I could have directly provided the model with edited music or voice clips along with reference images for generation.

But for this project, I generated the clips first and then re-generated them to add lip-sync.

luokai@luok.ai

The Seedance 2 model is incredibly powerful, completely overshadowing all other models. This is an original video I created in just one day, though the music was previously made using Suno.

This was a habitual mistake I made while working on this video. Initially, I followed the traditional workflow for video models: first generating reference images, then describing the actions, and so on.

However, Seedance supports up to 9 images, 3 video clips, and 3 audio clips as reference materials simultaneously for each generated segment.

luokai@luok.ai

The Seedance 2 model is incredibly powerful, completely overshadowing all other models. This is an original video I created in just one day, though the music was previously made using Suno.

Seedance 2 automatically designs camera angles based on the content, though you can also specify camera movements precisely. In the raw clip below, I didn’t describe camera angles—you can compare it with my final video.

After generating the clips, I edited them by adding lip-sync, syncing them with the music, and adjusting the speed of some segments to match the beat.

luokai@luok.ai

The Seedance 2 model is incredibly powerful, completely overshadowing all other models. This is an original video I created in just one day, though the music was previously made using Suno.

1. Overall atmosphere description 2. Key actions 3. Scene description: starting pose, mid-sequence body/hand movements over time, and ending pose 4. Dialogue/lyrics/sound effects at specific timestamps

The Seedance 2 model is incredibly powerful, completely overshadowing all other models. This is an original video I created in just one day, though the music was previously made using Suno.

🔥 When rhythm takes over, power isn’t shown — it’s felt. OWN THE BEAT is raw Brazilian Funk stripped to its essence — no melody, just command.

youtu.beOWN THE BEATYouTube video by LUOKAI

Every chant, every breath, every siren hit pulses like a declaration of control. It’s not about dancing to the rhythm — it’s about being the rhythm. Minimal. Hypnotic. Absolute. youtu.be/rxWNmzQpW2c

The Seedance 2 model is incredibly powerful, completely overshadowing all other models. This is an original video I created in just one day, though the music was previously made using Suno.

In the past, producing a video like this would have taken me at least a week, and the quality wouldn’t have been nearly as good. Hollywood really needs to start rethinking its approach to content creation.

12 Jan

Finally, it’s official: Apple’s next AI leap is… built on Google’s Gemini. 🤯 Apple and Google have signed a multi-year agreement: future Apple Foundation Models will be based on Gemini models and Google Cloud technology.

12 Jan

This will drive upcoming Apple Intelligence features—including a more personalized Siri—while Apple continues to leverage on-device and Private Cloud Compute to maintain its industry-leading privacy standards.

A Meta Quest open-source MR app turns your room into a language lab. Spatial Lingo shows how mixed reality + AI can teach vocab by labeling your real world—now open-source.

MR-first UX via Passthrough. You’re learning in your actual environment, not a cartoon room. Roomscale + Hand Tracking + Voice = hands-free practice.

github.comGitHub - oculus-samples/Unity-SpatialLingo: Spatial Lingo is an open source Unity app for Meta Quest that helps users practice languages through real-world object recognition. Built with Meta SDKs, it...Spatial Lingo is an open source Unity app for Meta Quest that helps users practice languages through real-world object recognition. Built with Meta SDKs, it’s a template for mixed reality experienc...

Open-source foundation. Dev-focused sample from Oculus DevTech. Fork it, swap languages, tune models, and build your own MR learning experiences. It’s a baseline to prototype commercial-grade features without starting from zero. Github: github.com/oculus-sampl...

A Meta Quest open-source MR app turns your room into a language lab. Spatial Lingo shows how mixed reality + AI can teach vocab by labeling your real world—now open-source.

meta.comSpatial Lingo: Language Practice on Meta QuestSpatial Lingo is an open source showcase app for Meta Quest that transforms your space into an interactive language practicing playground. Instantly identify and translate real-world objects, practice...

Built for Meta Quest Passthrough, it detects objects around you, overlays translated words, and listens as you speak. A playful 3D guide gives real-time pronunciation feedback, turning your room into a dynamic classroom. It’s positioned as an open-source challenger to commercial MR language apps.

It identifies chairs, desks, and more, then overlays nouns/adjectives in your target language. The app listens and judges pronunciation strictly. That’s useful for serious practice, even if it feels tough. Expect real-time feedback and progression into a “final level” with sharper visuals.

Niji 7 just landed. The latest Niji focuses on sharper eyes, tighter coherency, and better prompt adherence. It keeps legacy flags and adds sref tweaks for style control. After 18 months of training, this release targets fewer misses and more faithful outputs for anime creators.

Prompt following for specifics. Niji 7 improves on complex, multi‑clause requests. It’s more literal with ordering and constraints, so you can stack attributes without losing key elements.

sref: style reference control. Use sref to steer aesthetic toward a target look while keeping your prompt. Handy for series consistency, brand vibes, or matching a particular artist’s feel.

Core: “Crystal Clarity.” Sharper reflections and eye details reduce muddiness in faces and highlights. Expect fewer artifacts in glossy surfaces and more readable micro‑features—think eyelashes, irises, jewelry.

Coherency: “what you ask is what you get.” Better compliance with spatial cues (left/right), colors, counts. E.g., “red cube left, blue cube right” renders correctly more often, cutting prompt wrangling.

Key stats: Coherency: major improvement vs prior Niji Prompt following: stricter left/right, color, object placement Compatibility: backwards support incl. –sv 4; use –niji 7 in Discord or “Version: Niji 7” on web

AI video is stuck in “fun”—the next leap is making it truly useful. Over two years since Sora’s splashy debut, generative video is dazzling but still struggles to land in everyday workflows. The missing piece?

The next step for AI video isn’t about being more “flashy,” but more “stable.” LuxReal’s approach is still in its early stages, but the direction is right. Below is the link—feel free to join the beta test. Share the product ads you create with LuxReal and let me know about your experience.

luxreal.aiLuxReal | Al-Powered Creator for Product VideosLuxReal creates high-quality product videos across beauty, electronics, PMCG, toys, food & beverage, and more. With just a single image, LuxReal generates cinematic, consistent product ads—delivering ...

I’ve connected with LuxReal and got three redeem codes for you to try more. Share your test results and videos in the comments—first three get the codes via DM. Try it now: www.luxreal.ai

- Multi-agent collaboration: Instead of a closed approach, it integrates industry-leading algorithms, emphasizing “multiple capabilities working together to produce a stable video.”

If AI video is to truly become a “tool,” I lean toward this path: first, ensure the video makes sense in a 3D world, then focus on style and flair. Controllability, reusability, and credibility—these all stem from spatiotemporal consistency.

- Choose the subject: A person or product—just upload an image. - Set the stage: Environment, actions, lighting. - Define the logic: Camera movement and subject motion. The steps aren’t complicated, but the core lies in how “stability” is achieved at the foundational level.

Two aspects of their underlying tech resonate with me: - 3D generative model (Lux3D): Converts text/images into 3D representations, making materials and volumes more realistic.

Stable, 3D-aware “spatiotemporal consistency” so objects, people, and scenes remain coherent across frames. LuxReal is laying the first brick on this path with a 3D-first, multi‑agent approach. Try it now: www.luxreal.ai At the end of the post, I have some free redeem codes for you to try.

LuxReal’s approach starts with 3D and integrates multiple capabilities to solve the “stability” problem. Their workflow is straightforward (which is why I think it’s practical):

HRM^2Avatar turns a single iPhone scan into a high‑fidelity, real‑time digital human—on mobile. ⚡ It combines mesh‑driven clothing deformation with illumination‑aware Gaussians, learning textures from static shots and dynamics from motion clips.

A fully GPU pipeline pushes sub‑frame responsiveness for AR/VR and social apps, making studio‑grade detail accessible. Key stats iPhone 15 Pro Max: 2048×945 120FPS Apple Vision Pro: 1920×1824×2 90FPS 533,695 splats; 5‑minute single‑phone capture

acennr-engine.github.ioHRM^2Avatar: High-Fidelity Real-Time Mobile Avatars from Monocular Phone ScansWe introduce HRM^2Avatar, a framework that produces photorealistic, personalized avatars from phone scans, supporting real-time rendering and animation on mobile devices. Combining dual-phase capture,...

Core idea: two‑stage monocular data. Static, texture‑rich images supervise Gaussian attributes; dynamic motion sequences optimize pose‑dependent deformation and lighting changes—bridging single‑view limitations with tailored signals. Project: acennr-engine.github.io/HRM2Avatar/

Meshy 6 just raised the ceiling for AI 3D characters—cleaner topology, sharper forms, truer anatomy. Upgrades that you’ll feel in production: — Refined Geometry: Cleaner, anatomically correct meshes that deform better when rigged.

— Hard Surface Boost: Crisper edges and tighter structure for mechanical + geometric models. — Low Poly Mode: Purpose-built for game devs; efficient wireframes optimized for real-time.

15 Dec 2025

Kling just gave video creators granular control over pacing—start/end frames with selectable durations. Key stats Start/End Frame duration: 3–10s Resolution modes: 1080p and 720p (same features)

15 Dec 2025

With VIDEO O1, you can define Start & End Frames between 3–10s for smoother transitions and precise timing. From punchy, high-impact beats to immersive cinematic moves, your story flows exactly as intended. Plus, there’s a 720p mode that keeps all the 1080p features for faster renders.

Gemini 3’s new Deep Think mode tackles problems by exploring multiple hypotheses at once ⚡ Positioned as Google’s most advanced reasoning tier, Deep Think runs iterative rounds to refine outputs—especially for complex tasks like code visualization, prototyping, and nuanced analysis.

It’s currently gated to AI Ultra subscribers in the Gemini app, with usage under the “Thinking” model + prompt bar toggle. Key stats Access: AI Ultra only Modality: iterative, multi-hypothesis Rollout: app-first

Kling AI just leveled up avatars to full 5‑minute performances. Wild. Avatar 2.0 is upgraded and expressive—built to handle explainers, ads, songs, and stories like real characters. Key stats 5‑minute avatar acts - Max expressions

Core idea: Full‑length avatar performances 5 minutes means complete narratives—intro, arc, CTA—without stitching multiple clips. Think product explainers or mini‑ads in one take, with consistent character and emotion. 🎬

Core idea: Max expressions = believable characters Richer facial cues and delivery land humor, urgency, and sincerity. This moves avatars from “demo” to “cast member” for social and ads. Example: a spokesperson that can smile, pause, and emphasize like a human.

For 12 hours only, they’re rewarding engagement with credits and picking 200 winners for a 1‑month Standard Plan, delivered via DM. Techy, playful, and very creator‑friendly.

youtu.beEngineAI T800: Born to Disrupt! #EngineAI #robotics #newtechnology #newproductYouTube video by Engineai Robot

EngineAI’s T800 claims real-shot humanoid speed and power—no CGI, just disruption.🤖 Positioned as a general‑purpose robot with peak performance, omnidirectional awareness, and long endurance. youtu.be/FGcQqyCaG5s?...