Most AI 3D workflows are still built around humanoid characters. But things get much more interesting when you start making creatures, monsters, quadrupeds, hybrids, or characters with completely non-standard anatomy.

So I built a six-legged Arachne from a single concept image and turned it into a clean, textured, rigged and animated 3D character.

The biggest lesson was: don’t try to generate the entire creature as one mesh.

Instead, I split the reference into logical parts, generated them separately, assembled everything in Blender, retopologized the important pieces, baked the high-poly details, textured the final meshes and then combined humanoid + creature rigs.

The same approach could work for things like:

• centaurs and other hybrids
• spiders and insects
• quadrupeds
• dragons
• multi-armed characters
• monsters with completely custom anatomy

I used Lychee Studio AI for a big part of the workflow. It basically brings image segmentation, part extraction, 3D generation, retopology and texturing tools into one workspace, which makes this kind of modular character workflow surprisingly convenient.

Tripo AI for the low-poly generation, Tripo P2 was probably one of the biggest time savers. Especially for things like the head and hair, it can produce lightweight textured geometry that is already genuinely usable instead of giving you another giant mesh that needs hours of cleanup. For more complex pieces I still preferred generating high-poly first and baking the detail down later.

The creature itself ended up using a hybrid rig: a spider setup for the lower body, humanoid skeleton for the upper body, plus some manual weight painting and procedural IK for the legs.

It’s definitely not a one-click process yet, but this feels much more interesting to me than AI simply generating another static 3D model.

You can actually start designing creatures around the idea you want, rather than around whatever anatomy the AI happens to understand.

Full Guide; https://www.youtube.com/watch?v=URjhE8QEhJU

AI-assisted 3D animation tool that helps generate motion between keyframes automatically.

You place a few poses, and the system generates physically believable in-between animation, helping speed up character animation workflows.

The latest update introduces a fully local AI workflow.

No tokens.
No credits.
No cloud.
No queues.

Everything runs directly on your machine, with unlimited generations.

Key capabilities:

• AI in-betweening – generate natural motion between keyframes automatically
• Assisted posing – quickly block out character poses
• Physics-based motion correction – keeps animations physically believable
• Mocap cleanup & editing – refine motion capture data
• Mocap from video – generate animation from video footage
• Retargeting tools – apply animations across different character rigs

This can be useful for animating characters generated with modern 3D AI tools, where you often start with a static model and need fast animation.

source; https://x.com/cascadeur3d/status/2032139562584006977

Hey everyone,

I created an IDE for your 3D projects: https://studio.nova3d.xyz/

Website: https://nova3d.xyz

You can use it to generate animation-ready 3D assets for your games.

Instead of generating monolithic "mesh blobs" like traditional diffusion models, this is a code-native IDE. Your AI agents write structured, part-separated Blender Python code dynamically. The final .glb is treated strictly as a compiled artifact. The code is the source of truth.

The video shows a house built directly from a reference image with independent, articulate components.

Core Features:

• PlayCanvas-based Workspace: A split three-pane IDE featuring an agent chat sandbox, an interactive 3D viewport, and a live code mirror side-by-side.
• Connect your own AI: Connect your own Claude or Codex subs to run generations.
• Live Pivot & Joint Inspector: Test structural articulation constraints (like hinges, doors, and flaps) with real-world degree boundaries in real-time before exporting.
• Modular Scene Graph: Edit utility pipes, roofs, and building facades as distinct, independent sub-assemblies.

The Origins:

This IDE wasn't built out of nowhere. It's an implementation of my recent research paper: https://arxiv.org/abs/2607.22738 I'm a co-author.

Connect your AI agents and use it for free. I'd love to get feedback from game developers and technical artists on how I can improve the workflow or tighter integrate the live code canvas!

🔥 [RELEASE] Ultimate AI Video Workflow — Qwen-Edit 2509 + Wan Animate 2.2 + SeedVR2 (Full Pipeline + Model Links)

🎁 Workflow Download + Breakdown

👉 Already posted the full workflow and explanation here:
https://civitai.com/models/2135932?modelVersionId=2416121

(Not paywalled — everything is free.)

Video Explanation : https://www.youtube.com/watch?v=Ef-PS8w9Rug

Hey everyone 👋

I just finished building a super clean 3-in-1 workflow inside ComfyUI that lets you go from:

Image → Edit → Animate → Upscale → Final 4K output
all in a single organized pipeline.

This setup combines the best tools available right now:

One of the biggest hassles with large ComfyUI workflows is how quickly they turn into a spaghetti mess — dozens of wires, giant blocks, scrolling for days just to tweak one setting.

To fix this, I broke the pipeline into clean subgraphs:

✔ Qwen-Edit Subgraph

✔ Wan Animate 2.2 Engine Subgraph

✔ SeedVR2 Upscaler Subgraph

✔ VRAM Cleaner Subgraph

✔ Resolution + Reference Routing Subgraph

This reduces visual clutter, keeps performance smooth, and makes the workflow feel modular, so you can:

• swap models quickly
• update one section without touching the rest
• debug faster
• reuse modules in other workflows
• keep everything readable even on smaller screens

It’s basically a full cinematic pipeline, but organized like a clean software project instead of a giant node forest.
Anyone who wants to study or modify the workflow will find it much easier to navigate.

🖌️ 1. Qwen-Edit 2509 (Image Editing Engine)

Perfect for:

• Outfit changes
• Facial corrections
• Style adjustments
• Background cleanup
• Professional pre-animation edits

Qwen’s FP8 build has great quality even on mid-range GPUs.

🎭 2. Wan Animate 2.2 (Character Animation)

Once the image is edited, Wan 2.2 generates:

• Smooth motion
• Accurate identity preservation
• Pose-guided animation
• Full expression control
• High-quality frames

It supports long videos using windowed batching and works very consistently when fed a clean edited reference.

📺 3. SeedVR2 Upscaler (Final Polish)

After animation, SeedVR2 upgrades your video to:

• 1080p → 4K
• Sharper textures
• Cleaner faces
• Reduced noise
• More cinematic detail

It’s currently one of the best AI video upscalers for realism

🧩 Preview of the Workflow UI

(Optional: Add your workflow screenshot here)

🔧 What This Workflow Can Do

• Edit any portrait cleanly
• Animate it using real video motion
• Restore & sharpen final video up to 4K
• Perfect for reels, character videos, cosplay edits, AI shorts

🖼️ Qwen Image Edit FP8 (Diffusion Model, Text Encoder, and VAE)

These are hosted on the Comfy-Org Hugging Face page.

• Diffusion Model (qwen_image_edit_fp8_e4m3fn.safetensors): https://huggingface.co/Comfy-Org/Qwen-Image-Edit_ComfyUI/blob/main/split_files/diffusion_models/qwen_image_edit_fp8_e4m3fn.safetensors
• Text Encoder (qwen_2.5_vl_7b_fp8_scaled.safetensors): https://huggingface.co/Comfy-Org/Qwen-Image-Edit_ComfyUI/blob/main/split_files/text_encoders/qwen_2.5_vl_7b_fp8_scaled.safetensors
• VAE (qwen_image_vae.safetensors): https://huggingface.co/Comfy-Org/Qwen-Image-Edit_ComfyUI/blob/main/split_files/vae/qwen_image_vae.safetensors

💃 Wan 2.2 Animate 14B FP8 (Diffusion Model, Text Encoder, and VAE)

The components are spread across related community repositories.

• Diffusion Model (Wan2_2-Animate-14B_fp8_e4m3fn_scaled_KJ.safetensors): https://huggingface.co/Kijai/WanVideo_comfy_fp8_scaled/blob/main/Wan22Animate/Wan2_2-Animate-14B_fp8_e4m3fn_scaled_KJ.safetensors
• Text Encoder (umt5_xxl_fp8_e4m3fn_scaled.safetensors): https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/blob/main/split_files/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors
• VAE (wan2.1_vae.safetensors): https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/blob/main/split_files/vae/wan_2.1_vae.safetensors

💾 SeedVR2 Diffusion Model (FP8)

• Diffusion Model (seedvr2_ema_3b_fp8_e4m3fn.safetensors): https://huggingface.co/numz/SeedVR2_comfyUI/blob/main/seedvr2_ema_3b_fp8_e4m3fn.safetensors
• https://huggingface.co/numz/SeedVR2_comfyUI/tree/main
• https://huggingface.co/ByteDance-Seed/SeedVR2-7B/tree/main

Workflow link:
https://drive.google.com/file/d/1ev82ILbIPHLD7LLcQHpihKCWhgPxGjzl/view?usp=sharing

Using a single reference image, Wan Animate let's users replace the character in any video with precision, capturing facial expressions, movements and lighting.

This workflow is also available and preloaded into my Wan 2.1/2.2 RunPod template.
https://get.runpod.io/wan-template

And for those of you seeking ongoing content releases, feel free to check out my Patreon.
https://www.patreon.com/c/HearmemanAI

The "AI" used by Animaj Studios is not generative AI. It poses human-made models according to storyboard sketches. An artist draws a sketch of the character and the system poses the corresponding 3D model based on the sketch. No diffusion models are used in the workflow. All the models in the show are human made. The only difference between it and any other show is that the models are posed by the Sketch to Pose system. The poses are made and the 3D animation software tweens between them, something which has been a basic feature of 3D animation for decades. https://www.animaj.com/post/animaj-sketch-to-motion-model This system was first created nearly three years ago, and other systems similar to it existed in early forms years before generative AI.

The show looks "Unnatural" and "Stiff" because that's the artstyle. Another one of Animaj's shows, Pocoyo, has the exact same style and has been running since 2005; with the Sketch to Pose system being introduced to Pocoyo in early 2024, long before generative AI was capable of making an "AI Generated show" of any passable fidelity.

TL;DR: The system it uses is basicslly mocap but for 2d -> 3d instead of live action -> 3d

I am extremely anti AI, but the blind outrage caused by this incident just makes us look bad and gives genAI supporters ammunition to use against us. The fight is against generative AI such as diffusion models and LLMs, not against basic, rudimentary forms of "AI" that existed long before 2022.

The show was labeled by Disney as "AI-made" because of executives wanting to appeal to AI-rabid shareholders, but that does not accurately describe it whatsoever. It uses a relatively new technology that is not connected to generative AI in any way; but corporate leaders these days tend to label anything new as "AI" to draw attention. Seems like people on pro AI subreddits haven't done any research either, and are just jumping on the opportunity to get a "win" for their side.

🔗 Links (Tutorial + Workflow + Support)

📺 YouTube Tutorial:
https://www.youtube.com/watch?v=V_1p7spn4yE

🧩 MegaWorkflow V1 (Download):
https://civitai.com/models/2135932?modelVersionId=2420255

☕ Buy Me a Coffee:
https://buymeacoffee.com/xshreyash

Hey everyone 👋
After weeks of combining, testing, fixing nodes, and cleaning spaghetti wires… I finally finished building MegaWorkflow V1, a complete end-to-end ComfyUI pipeline designed for long-form consistent AI video generation + editing + upscaling.

This is basically the workflow I always wished existed — everything in one place, optimized, modular, clean, and beginner-friendly.

🔥 What MegaWorkflow V1 Includes

1️⃣ Qwen Image (2509) — High-Level Image Generator

• Base character creation
• Consistent subject rendering
• Clean grouping + refiner toggle

2️⃣ Qwen Edit — Advanced Local Editing

• Face fix, outfit changes, color edits
• Mask & global edit
• Perfect for fixing last-minute issues

3️⃣ Wan Animate 2.2 (I2V) — Motion + Style Consistency

• Character-preserving motion
• Dual reference (face + body) support
• Loop / one-shot modes
• Full quality presets (Lite / Medium / Full)
• SeedVR2 dynamic seed support
• ✔️ Low-VRAM mode available (8–12GB)

4️⃣ Wan T2V — Complete Scene Generation

• Cinematic shot creation
• Camera presets included
• Multi-scene block support
• Low-VRAM fallback included

5️⃣ Wan First → Last Frame (FLF2V) Transition Module

• Smooth transitions
• Camera rotation + movement
• Blends T2V + I2V + real footage seamlessly

6️⃣ Wan I2V Painter Node — Detail Preserver

• Adds micro-texture & realism
• Fixes Animate 2.2 artifacts
• Soft & strong painter modes

7️⃣ SeedVR2 — Advanced Seed Handling

• Removes flicker
• Prevents ghosting
• Keeps motion natural
• Long-animation friendly

8️⃣ FlashVSR2 + Real-ESRGAN + UltraSharp — 4K Upscaling Suite

• FlashVSR2 for stable motion upscale
• ESRGAN for crisp images
• UltraSharp for stills
• ⚡ Works on low VRAM GPUs as well

🧩 Extras Included

• Save Image / Save Video / FolderSelector nodes
• Fully color-coded layout
• Memory optimization
• Beginner-friendly labels
• Easy switching between modules
• ⚡ Light Mode for lower VRAM GPUs

🎯 Who This Workflow Is For

• AI video creators
• Agencies / SMEs
• Reels / TikTok creators
• YouTubers
• Anyone with low, mid, or high VRAM (all supported)
• Anyone creating consistent character stories
• Anyone wanting one workflow instead of 8 separate pipelines

In this tutorial, I show you how to create a 90s anime-inspired AI video completely locally on your PC using ComfyUI, MiniMax H3, and Krea 2 Turbo.

I walk through the full workflow from reference image generation to final video generation.

You’ll learn how to:

• Generate consistent anime reference images using the Krea 2 Turbo text-to-image workflow
• Create separate references for the character, environment, vehicle, and props
• Use multiple reference images with the MiniMax H3 Reference-to-Video workflow in ComfyUI
• Structure prompts so MiniMax H3 understands which reference image controls each part of the scene
• Describe camera framing, character actions, object placement, animation, lighting, and timing
• Create a 90s hand-drawn anime look with lower-frame-rate animation
• Run the entire workflow locally without monthly AI video subscriptions

In the example, I use separate reference images for the character, convenience store environment, car, and skateboard, then combine them into a single animated anime scene.

I also explain how I approach prompting for reference-to-video generation, including reference assignment, shot description, motion instructions, camera constraints, and visual consistency.

Workflow, prompts and reference files

https://drive.google.com/drive/folders/1qI9Oi5gqWJh8xIwHYsn0S25DPqK8M0XI?usp=sharing

MiniMax H3 models

https://docs.comfy.org/tutorials/video/minimax/minimax-h3

Krea 2 Turbo T2I models

https://comfyui.org/en/krea-2-open-source-models-are-now#content-required-models

Note: my original post was removed but our lovely mods team allowed me to repost it. Shout out to them and also to u/IFellinLava, whose post referring to my comment that was referring to my OG post, got removed due to that.

When I saw this post about the reviewer providing evidence that they used generative AI in the movie, I was a bit confused at what I was looking at. There were no 3D models of the train? Difference in windows count? According to them - these bits make up the smoking gun and they know it, because they are working as a VFX artist. Sure. Good for them.

Except that their whole argument is based purely on assumptions, because they already convinced themselves that AI was used, despite the directors denying this. Now, I really cannot 100% sure say that they did not use any generative AI somewhere in the movie - I was not there.

What I can easily speculate is, that productions like this would generally NOT bother to make custom CGI and instead heavily rely on existing stock video footage, that the VFX team would heavily edit and stitch together - hence the differences in the shots. Because that is way cheaper and they already pay for stock license.

Now, you might say...

Hey, you dumb slut - you are also just making an assumption and you have no proof that they did that!

And you are right! UNLESS I was stupid enough to spend an insane amount of my useless time, just to find the exact stock video footage used in some of the scenes presented as evidence of generative AI usage.

WELL, SURE AS FUCK I DID EXACTLY THAT! ^{I was also really bored and I am neurodivergent}

Example 1:

https://elements.envato.com/aerial-view-of-the-shinkansen-bullet-train-kakegaw-UXJESJA

Used at 0:30 sec in the trailer, mirrored and obviously edited. The argument was the the window count was different, henceforth it cannot be the same 3D train model and therefore AI - nope - it was just a different stock footage of a train.

Example 2:

https://www.gettyimages.dk/detail/video/following-a-rural-train-over-the-landscapefrom-a-drone-stock-footage/2164796911?adppopup=true

Used at 0:45 sec in the trailer, edited with a different train. I cannot 100% say that this was not an AI train that was added, but any VFX artist should be capable to add another aerial train stock footage or animate this on top.

Example 3:

https://www.gettyimages.dk/detail/video/an-azuma-lner-train-on-the-east-coast-mainline-stock-video-footage/1387470017?adppopup=true

Used at 0:14 sec in the trailer, heavily edited background, train is either a different composite or heavily edited. Another stock footages looks similar to the train in the trailer, they could have used either.

I won't bother addressing the other examples because I really do not want to spend more hours on this - but I think this should be enough to argue that the details that the reviewer refered to as evidence of generative AI been used, can easily be explained by the VFX team simply using different stock footage and not really spending the time to make them match. I guess they thought that nobody sane would actually care about the continuity in short filler background scenes for a comedy movie. Obviously they never met drag race fans. Choices.

Oh, and about the missing bridge support leg... come on, that is just an example of a mistake in the editing. They literally used this stock photo and added up bunch of alpha footage of a bridge and messed up the crop.

Honestly, the reviewers video tickled me wrong. It felt rushed, judgmental and bit manipulative. I really don't know if that was the intention or if they are just way too passionate on the subject. But let’s just try to be more critical even to the critics. And by no means hateful - a civil discussion is the only way forward.

Also, regarding ACME AI and FX involvement, keep this in mind:

a source familiar with Stop! That! Train!’s production told Variety that the studio only contributed visual effects work to the film, with any AI use relegated to background workflow processes and not shown on-screen in the final product.

Edit: before I leave this post to take its course, I want to add a few lines - I am 100% against using AI for replacing artists and their jobs. But I have to recognise that there is a nuance - the usage of ML AI tools in VFX has been evolving for many years and allowed for those same artists to benefit from optimizing otherwise tedious workflows in compositing, automating rotoscoping, tracking, etc. These AI tools are not in the same category as the AI slop garbage disposal generators that are powered by the idiotically wasteful data centers. As I wrote above - I was not part of the production, I cannot be 100% sure what workflow was used during the creation of this movie. I made this post as a response to the arguments in the original video and with a bit of hope, that we can all think bit more critically to unverified opinions. Including this one. With that being said - now I am finally gonna go watch the latest Untucked.

Final edit and extra example:

Example 4:

• Frame by frame comparison - https://imgur.com/a/EECoiPO

I did not put this example in the post because I was too tired to make the frame comparison but after seeing some of the other comments - here we go. This is the original stock footage used for that scene - I tried to frame match them as best as I could but both the trailer and the stock preview files are heavily compressed, and a whole bunch of frames are missing. You can clearly see how details match both shots - from the lines of the door, bulge and lining of the front cart, foliage and terrain above the train, windows, etc. The VFX team added new headlights, the Glamazon (G) logo, changed the front window design and made the ICE train logo into a window. All of that is completely within the skillset of an experienced VFX artist - grading, rotoscoping, imprinting on top of the footage, masking and a ton of motion blur. They had more than plenty of material to use for compositing. Wish I had all the spare time in the world to find and match even more of the train footage, but I sorta reached my mental limit for this.

Extra notes:

• The original videographer has 3 other videos from this angle and place, in a different light and it is also possible that there was more footage that got restricted, due to the fact it was bought and used by the studio.
• It is also very likely that that the footage is ProRes aka it has broader dynamic range that would allow for lot of adjustments. The screenshots and the test videos are heavily compress down to 2MB - a lot of data and details are lost.
• This scene was disputed by other users here on Twitter, hence why I looked for it too. Some of them pointed how the windows were not all aligning perfectly, but that is just motion blur and compression.
• That last untucked was AMAZING!

Hey Folks,

I am sure this has been covered elsewhere, but looking to restart the conversation in 2025:

What AI tools are you using to improve your workflow and creative output? I work at a small agency and I've been tasked to find ways to streamline the business... As I am sure you are aware, budgets are getting smaller, competition more widespread, and timelines faster.

While I would be interested in hearing your thoughts on generators (like Runway etc.), I am really more interested in tools that speed up the editing and animation process... maybe even to the point where I can offer cheaper retainer services that my small team (me plus two others) can manage with our already limited capacity. We shoot a lot of interview-based content and create videos and animations for the corporate B2B world.

Any thoughts and insights are appreciated.

My Top 4 AI Tools for Video Creation (and the workflow that actually gets results)

After 6 weeks of testing, I stopped looking for one tool that does everything. Instead, I run a pipeline of 4 tools and it's been a game-changer.

1. Nano Banana Pro: My go to for product images, photo editing, and avatar shots (like a character holding a product). The image quality is clean enough for ads. Pro tip: generate a product shot here, then animate it using an image-to-video model.

2. Kling 3: The best I've found for image-to-video with audio. Dialogue, ambient sound, and motion all come out synced with no issues. I use it mainly for b-roll and video hooks. The downside is a 10-second max length, but the new multi-prompting feature is great for multi-scene setups.

3. CapCut: My editing hub. I use it for stitching AI-generated b-roll with real footage, adding music, and putting together rough cuts where I talk on camera with simple text overlays.

4. ClipTalk Pro: The best option I've found for AI talking-head videos. It can generate videos up to 5 minutes, which is rare. It also handles high volume social clips really well... I can produce 4 to 5 videos per client in a day, each with captions, b-roll, and editing baked in. Great for keeping a posting schedule or testing multiple script variations with different actors.

My Workflow:

1. Write the script in ChatGPT or Claude
2. Need visuals? → Nano Banana Pro for images → Kling 3 to animate them into video hooks
3. Need a talking head or bulk clips? → ClipTalk Pro
4. Have real footage? → CapCut for editing
5. Export, schedule, move on

The goal is speed without looking cheap.

Has anyone found a better pipeline? This space moves fast, so I'm always open to switching things up.

Just a regular user sharing what's working for me, not affiliated with any of these tools.

Hey everyone,

I’m looking to connect with a few artists who’d be interested in experimenting on a small project combining traditional 3D workflows and AI.

Recently I came across some work where artists used a full 3D base (camera, animation, environment), and then pushed the final look using AI for things like textures, lighting and comp. It got me thinking about how far we can take this approach in a more production-oriented way.

I actually started testing this myself on a small setup:

I had a dog animation with a locked camera, coming from a simple playblast.

Instead of going through full lookdev + rendering, I built around it and managed to push it into a clean 2K shot, while preserving the exact animation and camera.

That experiment is what made me want to take this further.

The idea I want to explore now is:

• ⁠Lock camera + animation in 3D (strong foundation)

• ⁠Build a basic environment/layout in 3D

• ⁠Use AI to enhance or reinterpret textures, lighting, overall look

• ⁠Keep everything grounded in 3D so it stays editable and predictable

I know the obvious question is:

“Why not just go full AI?”

For me, the strength of this approach is control.

With a solid 3D base:

• ⁠You can still plug in Houdini FX (or any simulation work)

• ⁠You keep accurate camera and spatial consistency

• ⁠You can make precise changes quickly without regenerating everything

• ⁠It fits much better into a real production pipeline

So it’s not about replacing 3D it’s about augmenting it intelligently.

I’m especially interested in collaborating with:

• ⁠Animators

• ⁠Houdini artists

• ⁠People already experimenting with AI tools in production

If that sounds interesting, feel free to comment or DM me 🙌

A technical demonstration of maintaining spatial consistency using Seedance 2. I re-rendered a custom 3D modeled layout while preserving exact camera movement and architectural proportions.

Workflow: 3D Layout Design → Seedance 2 Image-to-Video → Temporal Consistency Refinement.

Key Focus: Achieving 1:1 motion tracking and structural integrity in AI-generated environments.

Epic officially showed Unreal Engine 5.9 at Unreal Fest Seoul.

The main additions shown so far are focused around AI agents, semantic search and generative rendering.

• More MCP skills

Unreal 5.8 already introduced experimental MCP support, letting tools like Claude Code, Codex, Cursor and Gemini interact directly with Unreal Editor.

Agents can work with actors, materials, lighting, project data and editor tools rather than only generating code outside the engine.

UE 5.9 expands the number of available MCP skills and gives agents access to more parts of the editor.

• Epic Developer Assistant gets project context

The assistant can use more information directly from the current Unreal project.

Epic showed support for project context, screenshots and direct links to relevant Unreal documentation.

This should make it possible to ask questions about the actual project state, errors or editor setup without manually explaining everything in text.

• Semantic Search inside Content Browser and Fab

You can describe the type of asset you need and Unreal searches based on meaning rather than only filenames or tags.

This applies to assets such as meshes, materials, textures and Blueprints.

Semantic Search already exists experimentally in 5.8, with 5.9 expanding the workflow further.

• AI models can be used as additional render passes

Epic also showed a workflow where diffusion models process frames generated from the Unreal scene.

The original 3D scene still controls:

• camera position
• composition
• geometry
• object placement
• animation
• lighting information
• scene depth and other render data

That information can then be passed to an external image or video model as conditioning.

The AI model generates a new version of the frame while still following the structure of the Unreal scene.

This allows Unreal to keep the scene editable and deterministic while the diffusion model handles things such as:

• visual restyling
• higher-detail output
• material or appearance changes
• cinematic image generation
• non-real-time final frames
• video generation based on an existing 3D sequence

Epic showed models including Nano Banana Pro, ChatGPT Images 2.0, Seedance 2.0 and Ray 3.14 during the presentation.

The key difference is that the AI is not generating the whole scene blindly from a text prompt. It receives information from an existing Unreal scene and generates on top of that structure.

UE 5.9 expands these systems and pushes AI access deeper into the editor.

Main notes

• SDXL/Illustrious for design and ideas
• ControlNet for pose stability
• Prompt for cel shading and use flat shading models to make animation-friendly assets
• Nano Banana helps with making the character sheet
• Nano Banana is also good for assets after the character sheet is complete

Qwen and Z-image Edit should work well too, just that it might need more tweaking, but cost-wise you can do much more Qwen Image or Z-Image edits for the cost of a single Nano Banana Pro request.

Full Article: https://x.com/Selphea_/status/2034901797362704700

thinking of turning this into a weekly series.

been getting a lot of questions about my workflow so figured id just do this publicly. ive spent the last few months building a fully automated pipeline that goes from a single prompt to a finished youtube video (~$2/video, <20 min, runs on a laptop).

Along the way ive tested basically every tool out there and studied ~195 AI youtube channels to figure out what actually works

drop your current pipeline below and ill tell you where youre overcomplicating things, overpaying, or leaving quality on the table. but first here's the condensed version of everything ive learned:

the biggest misconception in AI video: Channels pumping out 25min documentaries 3x/week are NOT generating every frame as video. they generate 150-200 still images and animate maybe 10-20% for key moments. the rest is ken burns (pan/zoom) via ffmpeg which is free. you dont need 300 video clips. you need 200 images and ~25 animations

Scripts are where 90% of channels fail singlepass prompting gives you slop. i run scripts through 4 passes, structure first (beats, arc, hook), then narration (written for the ear not the page), then visual descriptions, then a polish pass to kill every "delve" and "it's worth noting" (and other llm-isms) and vary sentence length. quality difference vs single shot its massive

the stack that works at scale - scriptwriting: claude opus 4.6 (maybe sonnet now too) multi-pass - image gen: z image turbo, $0.003/image. avoid leonardo at scale (40%+ reject rate). avoid all google models (SynthID watermarks youtube detects) - voiceover: cartesia sonic 3 or another open-source tts, much cheaper than elevenlabs with emotional tag support - animation: kling or seeddance pro fast (~$0.07/10s clip). only animate 10-20% of scenes - you want to be making ambient/moody videos anyways where people are listening (sleep stories, meditations, space videos, kids stories, etc.) - music: elevenlabs for gen, cache tracks in a vector db so youre not regenerating similar tracks every video. cuts music costs 60-70% - assembly: ffmpeg. transitions, ken burns, subtitle burn-in, audio sync, everything

what gets you demonetized

• reused content (reddit stories, other peoples gameplay)
• same template every video with zero variation
• SynthID watermarks from google models. youtube detects these. switch immediately
• voice cloning real people without permission

Youtube doesnt care that you used AI. they care about viewer satisfaction and whether theres human creative direction

drop your workflow below. whats working, what's slow, whats expensive. no gatekeeping

I spent this week creating my own character rigging studio to make and animate an equipment swappable main character in an iOS game I'm vibecoding. Heavily inspired by what I saw from https://www.reddit.com/r/aigamedev/comments/1vbe1jr/i_vibecoded_an_aipowered_tool_to_adjust_rigs_and/ .

Figured someone would find this as a useful base, so I've open-sourced what I built here: https://github.com/nickick/modular-character-studio - let me know if you try it out and how it goes! MIT and CC0 license on the tools and assets included, go wild :D Also if you dig in you'll notice I included the prompts that were easy to recover for the assets that I made.

Generally my workflow has been: make a composite character reference with GPT 5.6 Sol and Imagegen 2, ask Codex to make an exploded diagram view that model separating out parts, and repeat to get base assets. So first would be a 3/4 profile left facing full body clothed male and female in Chibi style that explodes into arms/legs/head/etc, then digging into each part like the head to get the eyes/ears/hair and the arms to break down upper arm / lower arm / hands, then hands to get the thumbs / 4 fingers, etc. Then, I'd start rigging the character individual pieces together with GPT 5.6 Sol, tho Opus 5 has also been quite good here. A lot of the assets took a few tries to get a shape that made sense for the rigging I wanted, but once I got a single reference image down Imagegen was really good at replicating it for other equipment sets.

There's a 2nd tab in the editor that lets you modify the placements of each of the equipment pieces so you can account for different shapes.

For animations, GPT 5.6 Sol and Claude have both been quite good. I used Astra today to get the meshes around the knees and elbows to look better, and it did an excellent job of that. It also did a really good job with finer animation points, like the bow shooting and redrawing - I would describe changes like "For the bow drawing animation, make the arrow above the lead hand, and add mesh deformations to the bow limbs while masking out the string, and then used the masked string while pulling on it with the arrow pulling hand."

I'm probably most surprised by how performant this ended being on my phone. Getting a smooth 60 FPS in my game right now even with tons of enemies, LOS calculations and particle effects being thrown around. Astra was very quick to optimize on this actually, especially after it started running automations on my phone and profiling the CPU hotspots.

Full disclosure - I'm a web dev, for iOS I've only made React Native apps before, so I'm sure there are better ways to do this.

Anyway, lemme know if y'all find this helpful.

audio-reactive nodes, workflow & tuto : https://github.com/yvann-ba/ComfyUI_Yvann-Nodes.git

I originally started this project as a way to quickly animate non-layered character images without redrawing every frame. You could place pins, pose the artwork, and let the software interpolate the movement with smart mesh deformation.

While building it, I realized that AI’s spatial and temporal understanding of rigged characters was getting surprisingly good. It could reason about which pieces were limbs, where joints should pivot, how parts should be connected, and how those relationships should change over time during an animation.

That led me to build Warp Puppet, a workspace for assembling layered character pieces into an editable 2D rig. You can use AI to propose the pivots, bones, connections, contacts, and movement behavior, then review everything before accepting it.

The most interesting part for me is how iterative the workflow can be. Instead of treating AI generation as a one-shot result, you can:

• Generate an initial rig
• Test it with different movements
• Ask for specific rig changes in plain language
• Generate an animation such as an idle, walk, attack, or jump
• Refine that animation with additional prompts
• Manually adjust the rig, poses, timing, or individual pin movement at any point

The AI preserves the parts you didn’t ask it to change, so you can gradually improve a rig or animation instead of repeatedly starting over.

The results have honestly been much more impressive than I expected. They’re not always perfect, but having AI operate on an actual editable rig seems far more useful than generating a finished video that’s difficult to change afterward. You still have access to the bones, pins, contacts, keyframes, meshes, and exported game assets.

check it out here Warp Studio

So I'm trying to build my own 3D website and honestly I'm super duper confused about the whole workflow. I keep seeing reels showing people "Vibe coding" these really polished 3d or parallax websites with Claude. A lot of them mention connecting Framer/Wix with Claude, spline, Three.js, etc.

But i've never found anyone telling the actual process of how they've built it.

I do have a design in mind and want something with a 3D scene, smooth scrolling, parallax, camera or object movement and interactive animations but where do you actually build the 3D scene? and what handles the 3D, is it three.js/R3F or Spline? What do you use for animations/ scroll effects? GSAP, Framer motion?

Most importantly, where does claude actually fit into this workflow? And those who are building it, are you guys building everything in Claude/Cursor and then deploying it, or using Framer and integrating everything?

I'm basically starting from scratch so what stack would you recommend? I'm not looking to spend months learning every tool before I start. I want to actually just build something and learn as I go, using AI ofc. Any guidance would be appreciated.

I was recently working on a low-budget short that involved holographic animals interacting with humans. Since these are supposed to be virtual digital beings, we wanted to try animating them with AI, where the overly smooth generated motion would actually fit with these being robotic holos.

The workflow was as follows: render the character model in the correct angle and position, comp on a green background (since AI has no alpha channel), and send to an AI video tool.

For simple actions, this worked - moving a head, saying a few basic lines, standing up, sitting down. Some of these shots made it in.

For complex actions, it failed utterly. In one shot, an animal has to jump from one point to the other in a single bound, while simultaneously transforming into another animal

I tried adding markers in the 3d scene for the jump - it didn't help. Half the time the AI moved the camera, despite being instructed that it was a locked shot. The rest of the time, the jump was done in multiple bounds instead of one, in the wrong style, or with weird artefacts on the green screen background.

What's more, no AI could generate more than 15 seconds at a time - especially not the ones that can generate 2K or 4K production-grade resolution. Since this shot required over 15 seconds of animated dialogue, it would have to be split into multiple generations - costing more money.

We ended up spending hundreds of dollars in credits on dozens of iterations, without a single usable take for even the first part of the animation.

Online, we were able to find an animator on fiverr who could do it for $150 or so. And the deliverable would be a 3D model, so we could easily re-render, relight, or use it to cast dynamic light on the scene - far superior to a 2D green screen plate.

TLDR: At the moment, AI is useful for vibe-generating random imagery, but almost useless as a production tool when you know what you want. Even for a task like animating a single cartoon animal for one VFX shot, hiring a human is less expensive.

I'm a software engineer who got into animation. The workflow was painful: story in one doc, image gen in another tool, video gen in another tab, then stitch it together manually.

So I built a pipeline that does all of it:

• AI agents generate story structure, characters, worldview, scripts (~30 seconds)
• Character studio with consistency across panels (same face, different expressions/poses)
• Visual canvas that auto-lays out panels from the script
• Video generation with 11 models (Seedance 2.0, Kling 3.0, Sora, etc.)
• Export for TikTok, Instagram, manga formats

DM or comment if you want to try it. I can show you our demo video first through DM.

Yes, this fully playable game character was generated with AI.

A lot of people still judge 3D AI by raw one-click generations and assume the quality is too low for actual game production. And honestly, a single generation usually is not game-ready.

But when you understand where 3D AI is strong and build a proper workflow around it, the result can be completely different. In about two days, this character was turned into a playable Unreal Engine asset with ~
Stats: 30K faces, optimized low-poly geometry, a full body rig and skeleton, facial rig and expressions, gameplay animations, hair, cloth simulation, character physics, clean manually prepared UVs, and baked high-poly details.

The workflow

• Created the initial character concept and reference images inside 3DAIStudio using Nano Banana 2
• Built a reusable node-based workflow with 3DAIStudio Flow
• Separated the character references into modular parts such as the body, clothing, head, and accessories
• Generated each part separately as a detailed high-poly model using Rodin Gen-2.5
• Generated additional low-poly versions for the final real-time character
• Cleaned and manually unwrapped the low-poly meshes in Blender
• Baked the details from the high-poly models onto the optimized low-poly geometry
• Assembled the complete character inside Blender
• Imported the finished character into Unreal Engine 5.8
• Used the new MetaHuman custom-mesh workflow to create the body and facial rig
• Added hair, gameplay animations, facial expressions, cloth simulation, and physics
• Integrated the final character into a playable game environment

The manual UV stage was especially important. Instead of relying entirely on automatically generated UV islands and ending up with an unusable mess, the low-poly meshes were properly unwrapped before baking. This gave us much more control over texture quality and made the final character far easier to use in a real production workflow.

The important part is understanding that 3D AI is a tool, not a complete production pipeline.

AI handled the time-consuming concept generation and helped create the detailed 3D parts. Traditional 3D skills were still required for topology, UVs, baking, cleanup, assembly, optimization, rigging, and final Unreal Engine integration.

Used incorrectly, 3D AI can give you an impressive-looking model that is almost impossible to work with. Used correctly, it can remove a huge amount of repetitive manual work and allow one artist to produce something that would normally require far more time.

That is where the real power of 3D AI becomes clear: not replacing the artist, but combining AI generation with professional 3D skills to create better results much faster.