u/Delicious-Shower8401

The idea was to first generate a clothing reference with ChatGPT Image 2, then split it into separate pieces: top, bottom, boots, and hat. After that, I generated each clothing piece separately in Hitem 3D 2.1v and fitted everything onto a free basic mannequin from Sketchfab.

Workflow:

• Generated the original outfit reference with ChatGPT Image 2
• Split the concept into separate parts: top, bottom, boots, hat
• Generated each piece in Hitem3D 2.1v
• Fitted the outfit onto a free mannequin from Sketchfab
• Did minimal cleanup in Blender
• Quick optimization with decimate
• Slightly boosted the textures and fixed the material nodes
• Rigged with Mixamo / AccuRig
• Imported into Unreal Engine
• Retargeted the animation and set up cloth

Final result:

• Full 3D outfit from one image
• Generated with Hi3D 2.1v
• Around 12K faces for the full outfit
• PBR textures
• Minimal manual cleanup
• Around 1-2 hour total workflow

Not perfect, but for solo devs and indie devs this feels like one of the fastest ways to get usable 3D clothing for a character with 3D AI.

P.S I didn’t record the full guide because this was just a quick test for my own needs. I had a specific task, tried this workflow, and the result turned out pretty decent. If people are interested, let me know in the comments and I’ll make a short but detailed guide explaining the full process

fal just open-sourced 3DREAL, a new render-to-real IC-LoRA for LTX-2.3.

The idea is simple but very useful: take a rough 3D / CG / game render and turn it into a more photorealistic cinematic video, while keeping the original composition, camera movement, and scene layout.

So instead of asking AI to generate the whole shot from text, you can start with an actual 3D scene first.

Example workflow:

Generate or create 3D assets
Build a rough scene in Blender or a game engine
Animate the camera or objects
Render a simple 3D / CG pass
Use 3DREAL as the final render-to-real AI pass.

Highlights:

• Built for 3D / CG / game render inputs
• Works with Blender blockouts, game-engine renders, viewport animations, and synthetic 3D scenes
• Preserves the original composition and camera movement
• Can turn rough 3D renders into more realistic cinematic video
• Uses the trigger word 3DREAL
• Can be run directly on fal without local setup
• Model weights are available on Hugging Face

There are two versions:

3DREAL Light
More faithful to the original input, better structure preservation, fewer hallucinations.

3DREAL Strong
Pushes harder toward realism and detail, but can drift more from the original render.

You can build the shot in 3D first, control the camera, scale, layout, and timing, then use AI as the final render pass.

This feels much more practical than pure text-to-video for 3D artists, Blender users, and game devs.

Hugging Face: https://huggingface.co/fal/LTX-2.3-3DREAL-LoRA

This is probably one of the most practical AI video workflows for 3D artists.

Instead of generating a video only from text, you can build the scene in 3D first:

AI-generated 3D assets → Blender scene → camera animation → simple 3D render → AI render-to-real pass.

The big advantage is control.

You control the composition, camera movement, object placement, scale, timing, and layout in 3D. Then AI is used more like a final render engine to make the result cinematic or photoreal.

That feels much more useful than hoping a text-to-video model randomly understands the shot.

The 3D scene gives structure.
AI improves the final image.

This could become a very strong workflow for solo creators, game devs, and 3D artists.

Guide: https://www.youtube.com/watch?v=SYoGakzBHwM

FiCA is a new research project from Meta’s Codec Avatars Lab that turns a single portrait image into a photorealistic, drivable 3D Gaussian head avatar.

The wild part is the speed: the project page claims it can generate the avatar within 5 seconds, and the result can be animated in real time using target expressions.

What makes it interesting is that it is feed-forward, so it does not rely on slow person-specific test-time optimization. Instead, the pipeline combines human-centric vision foundation models, UV-space diffusion, feed-forward refinement, and a universal prior model to generate a Gaussian Codec Avatar.

Main points:

• single portrait image as input
• photorealistic 3D Gaussian head avatar output
• around 5 seconds generation time
• real-time expression driving
• no person-specific test-time optimization
• from Meta’s Codec Avatars Lab

This feels like a strong direction for digital humans, NPCs, virtual avatars, and real-time character workflows.

Not a classic game-ready mesh pipeline yet, but as 3D AI avatar generation research, this is definitely one to watch.

source - FiCA project page / arXiv

Workflow: Nano Banana for the concept → Hitem 3D 2.1v for the 3D model generation → quick cleanup in Blender → 3D printing.

The cool part is that this is no longer just “AI made a random mesh.” The result is actually usable enough to move into a real printing workflow with only some manual cleanup before printing.

A solid example of an image-to-face workflow in Unreal Engine 5 using 3D AI generation as the starting point.

The base was generated with Hitem3D 2.1v, and the interesting part is that it already gets roughly 70–80% of the likeness before the manual production work starts.

After that, the result still needs the usual cleanup and refinement: sculpting, topology adjustment, grooming, texture work, and setup for the final UE5 / MetaHuman-style pipeline.

So it’s not really a one-click final result, but it shows where 3D AI generation is becoming genuinely useful: getting a strong likeness base fast, so the artist can spend more time polishing instead of starting completely from zero.

Pipeline:

• Source image / likeness reference
• 3D AI generation with Hi3D
• Likeness cleanup and sculpting
• Topology / MetaHuman-style workflow
• Grooming and texture refinement
• Final setup in UE5

For production, I think this kind of workflow makes the most sense right now: AI gives you the first strong base, and the artist pushes it into something actually usable.

guide/source - https://www.youtube.com/@elvis-morelli

I tested the new experimental MCP support in Unreal Engine 5.8 with Claude Code.

What makes this important:

• Unreal Engine 5.8 now ships with experimental MCP support
• AI agents like Claude Code can connect to the editor
• The workflow is not just “chat with AI”, it is closer to AI-assisted editor control
• It can inspect project context, selected actors, assets, levels, materials, meshes, and more
• Developers can extend the toolsets with their own functionality
• It works locally through the Unreal MCP server

But to be clear, this is still experimental.

It is not a magic “make my full game” button yet. Some things work, some things are limited, and you still need to understand Unreal, project structure, assets, Blueprints, and what the agent is actually doing.

For me, the most interesting part is not that Claude can write code. We already knew that.

That could become huge for prototyping, debugging, asset setup, Blueprint assistance, level iteration, optimization checks, and repetitive editor tasks.

Early, rough, experimental — but definitely worth watching.

Full Review: https://www.youtube.com/watch?v=I5WLl4MdK28
X - https://x.com/Stefan_3D_AI/status/2069822819295523276

Stability AI just released Arbor, a new research model for controllable text-to-3D generation.

The interesting part is that Arbor does not rely only on a text prompt.
You can guide the generation with actual 3D constraint meshes:

• Hull: where geometry should exist
• Avoidance: where geometry should stay empty
• Touch: where the object should make contact or remain usable

So instead of just asking for “a chair” and praying to the random seed gods, you can define the space the asset should occupy, avoid, or touch.

This is pretty important for real 3D workflows because prompts are usually bad at precise spatial control. If you need a prop to fit a specific shape, leave clearance, match a contact surface, or follow a rough blockout, this kind of control makes way more sense than endlessly rerolling generations.

Arbor includes:

• text-to-3D generation with explicit geometry controls
• public inference pipeline
• mesh export
• curated examples
• condition metrics / evaluation tools
• Blender add-on workflow
• final mesh output through TRELLIS

Small note: this is an inference-only release. Training code, dataset construction, benchmark launchers, and internal evaluation wrappers are not included.

Still, this is one of the more interesting directions for 3D AI: not just “generate me something”, but “generate something that actually fits my design constraints.”

GitHub: https://github.com/Stability-AI/arbor
Model: https://huggingface.co/StabilityLabs/arbor

Unreal Engine 5.8 added a new AI-powered Markerless Mocap workflow for MetaHuman Animator, and this honestly looks like one of the most useful animation updates in UE recently.

Highlights:

• Capture body and face performance from a single off-actor camera
• No mocap suit
• No tracking markers
• No helmet camera
• Works with webcam or regular video footage
• Powered by Meshcapade Markerless Motion Capture
• Processing runs locally on your own machine
• Animation is generated directly inside Unreal Engine
• Captured motion can be refined in Sequencer
• Available as the MetaHuman Animator Markerless Motion Capture Plugin on Fab
• Free to use
• Currently Experimental, so expect some cleanup and limitations

The demo already looks surprisingly strong, especially for indie devs, cinematic artists, previs, solo creators, and quick animation blocking.

Fast movement, foot sliding, and extreme poses will probably still need manual fixing, because apparently reality still refuses to export clean animation curves.

But body + face capture from normal video, inside Unreal Engine, for free, with no suit or markers, is a pretty huge step forward.

One person, around 48 hours, one simple idea: a playable Unreal Engine 5 roguelike shooter built with AI-generated 3D assets.

The idea was simple and stupid in the best way possible: Rick Cucumber as the main character, fighting rat enemies in a small stylized shooter arena))

The full workflow:

1. Concept stage We started with the core idea and generated character / enemy concepts in NanoBanana2 using simple prompts.
2. 3D character generation The main character and rat enemies were generated in Tripo AI from the concept direction.
3. Rigging and animation After that, the characters were rigged and animated with AccuRig.
4. Environment assets We also generated environment pieces in Tripo P1 street houses, props, small scene elements, and general level dressing assets.
5. Unreal Engine 5 assembly Everything was brought into Unreal Engine 5 and assembled into a playable prototype.

For gameplay logic, we used a paid roguelike shooter template / shooting template as a base, so the focus of this project was not building all gameplay systems from zero.

The main goal was to test the AI-assisted 3D production pipeline: concept art → AI-generated 3D characters → rigging → animation → environment assets → real-time UE5 gameplay.

This was made over one weekend by one person as a small indie-style experiment / showcase.

Full Guide: https://www.youtube.com/watch?v=Kv3ajOok7_I

AI 3D generation becomes much more useful when it moves beyond a single static model. For this experiment, we built a full modular wardrobe system using Tripo 1.0 Smart Low Poly Mesh, where one character can switch between different hats, clothes, pants, skins, shoes, accessories, and animations.

The idea was simple: instead of generating one finished character, create a customizable 3D character system around one base mesh.

Workflow:

• Prepared the idea board and collected references for the character, outfits, skins, and accessories
• Generated the base character and all modular parts with Tripo AI
• Used Tripo P 1.0 Smart Low Poly Mesh to keep the assets lightweight and easier to use in real-time
• Assembled everything in Blender, adjusted scale, proportions, and fitting between the character and wardrobe pieces
• Rigged the full character setup in AccuRig
• Brought the rig back into Blender for cleanup, checking weights, fixing small issues, and preparing the final export
• Exported everything into Unreal Engine for setup and testing
• Built a small web app where users can switch skins, outfits, accessories, presets, and animations

This is the part that feels important for game dev and interactive 3D workflows.

AI can already generate base meshes, but the real value starts when those meshes become modular, rigged, customizable, and usable inside an actual product or game pipeline.

Not just one AI-generated character.

A character system.

FULL GUIDE: https://www.youtube.com/watch?v=onGjG6bxBIk

4D Gaussian Splatting feels like one of the most interesting directions for the future of video.

The idea is simple but powerful: instead of watching a flat 2D clip, the footage becomes a dynamic 3D scene over time. You can pause it, move the camera, reframe the shot, and view the moment from a different angle.

That is why 4DGS feels less like a normal video codec and more like a new spatial video format.

What makes it even more interesting is that this direction is already moving toward normal capture devices like iPhones. Single-camera footage is still much harder than a proper multi-camera setup, because the system has to deal with missing angles, occlusion, unstable depth, and unseen geometry.

But this is exactly where AI / neural rendering becomes important.

Instead of only storing frames, these systems learn a dynamic 3D representation of the scene, using Gaussian Splatting, camera poses, point clouds, and neural deformation over time.

Potentially, this could become useful for:

• spatial video
• VR / AR capture
• VFX and virtual production
• interactive 3D scenes
• game cinematics
• digital twins
• future video platforms where the viewer can control the camera

It is still research-heavy, not a perfect “upload any phone video and get magic 4D” tool yet.

But the direction is very clear: video is slowly becoming interactive 3D space.

Open-source project: 4DGaussians

AI tools are lowering the barrier to entry for anyone who wants to start creating in 3D!

The character was generated with Rodin Gen-2.5, then processed through Smart Low-Poly Mode to get a more optimized mesh, decent UVs, and a cleaner structure for real-time use. The final version uses around 25K polygons with 4K textures.

Workflow:

• created the initial character concept from scratch / sketch
• refined the image with Nano Banana 2
• split the design into separate parts: head, body, clothing, halo, and accessories
• generated each part separately in new Rodin Gen-2.5
• ran the generated models through rodin's Smart Low-Poly Mode
• assembled everything in Blender
• did small manual cleanup and optional Decimate adjustments
• rigged the character for free in Mixamo
• imported and tested the final character in Unreal Engine 5

The goal was not to make a perfect AAA character, but to see how quickly someone can create a usable low-poly 3D character without advanced modeling skills, starting from only one image.

For me, the most interesting part is the pipeline itself: 3D AI generation, smart low-poly optimization, decent UVs, 4K textures, free rigging, and a working UE5 character with minimal manual work.

P.S. I am building this for my own indie game, mostly in my free time outside of work. It is just a fun experiment

Tractive is an AI-assisted retopology tool designed to make one of the most repetitive parts of the 3D workflow faster: turning messy high-poly or generated meshes into cleaner, more usable topology.

And honestly, retopology is exactly the kind of place where AI makes sense. It is important, technical, and production-critical, but it is also one of the most tedious and least creative parts of asset preparation. A good AI tool here does not need to replace the artist. It just needs to remove the boring cleanup work and help artists get to rigging, animation, and game-ready assets faster.

We already posted about Tractive earlier, but at that time it was mostly an early demo with limited details. Now there is more information available, the tool is being tested, and according to the latest update, it is expected to roll out soon / next month.

Key highlights:

• AI-assisted retopology for cleaner quad-based meshes
• Designed for animation-ready topology, not just visual cleanup
• Useful for rigging, deformation, and character workflows
• Fast annotation-based workflow for drawing topology direction, loops, and boundaries
• Topology recalculation while editing, instead of fully manual rebuilding
• Stitching and region-based control for cleaner mesh flow
• Practical for characters, creatures, props, and game-ready 3D assets
• Potentially very useful for AI-generated 3D models, where topology is often the weakest part of the pipeline

This is one of the more practical AI use cases in 3D. Not “press one button and replace the artist,” but a tool that can fit directly into real production workflows: generate or sculpt a model, clean the topology faster, then move into rigging, deformation, animation, and Unreal / Unity-ready assets.

If AI-generated 3D is going to become truly usable in production, tools like this will be extremely important. Mesh generation is only one part of the pipeline. Clean topology is what makes those assets actually usable.

Sources:
80 Level article
Developer update on LinkedIn

REST3D is a new research project that reconstructs physically stable 3D scenes from a single image.

The interesting part is that it does not just generate visually plausible objects. It tries to understand physical relationships in the scene, such as support, gravity, object placement, and inter-object structure, then refines the scene with physics constraints.

Highlights;

• single image to 3D scene
• simulation-ready reconstruction
• fewer floating / intersecting objects
• physics-constrained scene refinement
• interactive 3D demos in Isaac Gym
• VR interaction demo with stable objects

Big step for AI-assisted 3D scene reconstruction, especially for simulation, robotics, VR, game prototyping, and interactive environments.

source: https://shirleymaxx.github.io/REST3D

Claude Mythos is showing some surprisingly strong low-effort, zero-shot 3D scene outputs.

The examples look like full game-like environments with terrain, buildings, rivers, units, UI, smooth camera movement, and visible animation, all generated from a simple prompt!

What makes this especially interesting is that Claude Mythos is not even a dedicated 3D AI model. It is not specifically built for 3D asset generation or game world creation, but the results are still genuinely impressive.

And this is only a low-effort zero-shot test. If outputs already look this strong without much optimization, it raises a pretty interesting question: what could this kind of model do with more targeted training, better tooling, or deeper integration into 3D/game development workflows?

For fast prototyping, interactive mockups, worldbuilding, and AI-assisted game scene generation, this looks incredibly promising.

source: https://x.com/Lentils80/status/2062656502238703966

MAMMA is a new AI-assisted markerless motion capture pipeline that turns multi-view video into full 3D human motion.

Instead of using suits, markers, or a traditional mocap studio, it works with synced camera footage and reconstructs SMPL-X body motion through segmentation, dense landmark detection, and multi-view 3D optimization.

Why this is interesting.

• no mocap suit
• no physical markers
• works from multi-view video
• can use consumer camera setups / phones
• captures full-body human motion
• supports complex person-to-person interaction
• outputs motion data that can fit into Blender, Maya, Unreal Engine, or character animation pipelines

This is not a one-click production tool yet, but it shows where AI-powered mocap is heading: cheaper capture setups, less manual cleanup, and more accessible motion data for 3D artists, game devs, robotics, and digital humans.

github: https://github.com/cuevhv/mamma

TripoSplat is a new free AI tool for generating 3DGS from a single 2D image, released as open source.

It turns one image into a 3D Gaussian Splat asset, which can be useful for fast 3D previews, scene prototyping, AR/VR, simulations, and image-to-3D workflows based on 3DGS.

Key points:

• Single 2D image → 3DGS
• Free and open source
• Code and model weights released under the MIT License
• Export support for .ply / .splat
• Adjustable Gaussian count, up to 262k
• Official ComfyUI workflow support
• Compatible with Gaussian Splat viewers like SuperSplat and SparkJS

Overall, this looks like an interesting new open-source implementation for AI 2D-to-3D generation, especially for people working with fast 3D previews and 3DGS-based workflows.

github: https://github.com/VAST-AI-Research/TripoSplat

HF demo: https://huggingface.co/spaces/VAST-AI/TripoSplat

I built a stylized playable environment in Unreal Engine 5 using optimized low-poly AI-generated 3D assets.

The goal was not to make a single AI render, but to test how far AI-generated assets can be pushed into an actual real-time environment workflow. Most of the 3D assets were generated with Hitem3D / Hi3D 2.1, optimized into a low-poly style, then manually cleaned up, assembled, adjusted, and art-directed into a playable UE5 location.

The scene is an Asian-inspired cliffside house environment with stylized architecture, props, lighting, atmosphere, and a playable layout.

Workflow:

• Generated the main environment assets with Hitem3D / Hi3D 2.1
• Kept the assets optimized and low-poly for real-time use
• Created separate props and architectural pieces instead of trying to generate the whole scene as one model
• Cleaned up obvious mesh, texture, and UV issues
• Assembled the full location manually in Blender
• Adjusted scale, composition, placement, and scene readability by hand
• Imported the environment into Unreal Engine 5
• Built the playable layout inside UE5
• Added landscape, lighting, mist, atmosphere, and final polish
• Recorded the final result as a real-time playable environment, not just a static render

AI helped generate the raw 3D assets, but the final environment still needed human decisions for optimization, composition, layout, lighting, mood, and overall art direction.

full guide: https://www.youtube.com/watch?v=1aC11WDjCi4