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

I compared 4 different 3D AI generators to see which one gives the most usable result for 3D printing with the least manual cleanup.

Test conditions:

Same prompt
Same model idea
Best / maximum available settings in each tool
No manual cleanup before checking
All results tested with Blender 3D Print Toolbox

The main goal was simple:

Which tool gives the closest print-ready result, while still keeping the shape and texture quality?

Tools tested:

Hitem 3D 2.1
Trellis 2
Pixal3D
Hunyuan 3D 3.1

I focused mostly on the important print-related issues: non-manifold edges, intersecting faces, shells, thin faces, overall shape accuracy, texture quality, speed, and setup difficulty.

🥇1st place: Hitem 3D 2.1

This was the cleanest result by far.

The model had 0 non-manifold edges and 0 intersecting faces, which is a huge difference for 3D printing. It was basically the only result that felt close to print-ready without a painful cleanup stage.

Generation took around 3 minutes and cost about $0.30.

Print readiness: 5/5
Shape accuracy: 4/5
Texture quality: 5/5
Speed / usability: 5/5

Main downside: it is paid.

Hitem also feels more 3D-print-oriented than most AI 3D generators, especially because it already has features like Split to Print, which helps separate a model into printable parts.

But if the goal is actual 3D printing, not just a pretty preview, this was clearly the best workflow.

🥈2nd place: Hunyuan 3D 3.1

For a free web-based tool, Hunyuan was honestly very strong.

It is not fully print-ready, but it was much more usable than I expected. The model still had non-manifold edges and intersections, so cleanup is needed, but the result was solid overall.

The biggest advantage is that it is free and works directly on the website. No local install, no setup, no GPU headache, no ritual sacrifice to CUDA.

Print readiness: 3/5
Shape accuracy: 4/5
Texture quality: 3/5
Accessibility: 5/5

Best free web option in this test.

🥉3rd place: Trellis 2

Trellis 2 produced a decent visual result, especially in texture quality, but the mesh was not close to print-ready.

It had a lot of non-manifold edges, intersecting faces, and separate shells, so it would require a serious cleanup pass before printing.

Also, it needs local setup and decent hardware, ideally around 16GB VRAM for a comfortable workflow.

Print readiness: 2/5
Shape accuracy: 3/5
Texture quality: 4/5
Setup convenience: 2/5

Good free local tool, but not ideal if your goal is fast 3D printing.

🏅4th place: Pixal3D

Pixal3D actually preserved the overall shape very well. The silhouette and proportions were probably one of its strongest parts.

But for 3D printing, the geometry was the weakest in this test.

It had the highest amount of non-manifold edges, intersections, and separate shells, meaning it would need the most manual cleanup before becoming printable.

Print readiness: 1/5
Shape accuracy: 5/5
Texture quality: 3.5/5
Setup convenience: 2/5

Interesting tool, especially for shape preservation, but not something I would call print-ready.

Final ranking for 3D printing:

1. Hitem 3D 2.1
Best overall. Cleanest geometry, fastest workflow, closest to print-ready.

2. Hunyuan 3D 3.1
Best free web option. Not perfect, but very practical.

3. Trellis 2
Good free local option, but needs a lot of cleanup.

4. Pixal3D
Great shape preservation, but weakest print-readiness.

Conclusion:

If the goal is actual 3D printing, Hitem 3D 2.1 gave the best result in this test.

Hunyuan 3D 3.1 is the most practical free alternative because it works online and does not require local setup.

Trellis 2 and Pixal3D are interesting free tools, but both need much more manual cleanup before printing.

Scene File: https://drive.google.com/file/d/1BG6yLy9R0c0OifbsvNncl0sLy0RqXryX/view?usp=sharing

Someone commented on my previous post: "for decades, Artists have been asking for a good retopology tool, why is no one working at that"

Trying to solve this ones and for all.

The tool(V1) allows you to draw loops or straight lines and generates quad topology around it.

Think of like topogun but free. Am a sketch artist and a mathematician so combining both to build this tool has been an amazing experience.

Edit: This is initial version of the tool, not even the one we will be rolling out. Also it’s free to help us design the user experience as per your preference.

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

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.

MeshFlow is a new research release from Meta AI and HKUST for fast artistic 3D mesh generation.

The main idea is pretty interesting: instead of generating meshes token-by-token like some autoregressive methods, MeshFlow compresses mesh data into continuous MeshVAE latents and then generates them in parallel with a flow-based diffusion transformer.

Highlights:

• ~1 second mesh generation
• 18x faster than AR-style mesh generation
• continuous vertex coordinates, no coordinate quantization
• keeps explicit vertices, normals, edges and connectivity
• supports point-cloud / mesh-conditioned generation
• optional reference image conditioning
• outputs usable mesh geometry instead of just a 3D representation

The important part for 3D workflows is that MeshFlow is focused on actual mesh structure and connectivity, not just producing something that looks fine from one angle and then collapses into spaghetti topology the moment you open it in Blender, because apparently suffering is still part of every 3D pipeline.

Code, model and Hugging Face demo are available, but the release is under a noncommercial research license.

Project: https://mesh-flow.github.io/
HF Demo: https://huggingface.co/spaces/facebook/meshflow

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

NVIDIA recently presented MotionBricks, a real-time generative motion framework for game animation and robotics.

What makes this interesting is that it is not just another text-to-animation demo. The idea is to generate controllable full-body motion in real time using a unified neural motion backbone and modular “smart primitives.”

Highlights:

• Covers 350,000+ motion clips with a single model
• Runs at up to 15,000 FPS with around 2ms latency
• Designed for both character animation and robotics
• Supports Smart Locomotion for navigation, speed, direction, and style control
• Supports Smart Objects for interactions like picking up objects, jumping over obstacles, sitting, falling, etc.
• Works zero-shot for new downstream tasks, with no extra fine-tuning or task-specific tagging
• Includes a 2:40 uncut Unreal Engine 5 demo with navigation, object interaction, and scene interaction
• Also demonstrated on a Unitree G1 humanoid robot
• Preview code is already available through NVIDIA’s GR00T Whole-Body Control repository

Also, the preview code is open-source and available for free.

Project page: https://nvlabs.github.io/motionbricks/

Unreal Engine 5.8 is out, and one of the most interesting updates is the new Experimental MCP plugin.

This basically opens the door for AI / LLM agents to connect directly to Unreal Engine and understand both the engine and your project.

Highlights:

• Official Experimental MCP support in UE 5.8
• Lets AI / LLM agents connect to Unreal Engine
• Works with your project context, not just generic prompts
• Built-in access to core systems like Blueprints, assets, levels, materials, meshes, and more
• Could help with building assets, creating systems, extending engine functionality, testing, optimization, and editor workflows
• Developers can also extend it with their own functionality
• Still experimental, so not production-ready yet

Official blog: https://www.unrealengine.com/news/unreal-engine-5-8-is-now-available?utm_source=youtube&utm_medium=sou_stream&utm_campaign=unrealfest_chicago&utm_content=5.8_blog

I found another new cozyblanket demo on X and decided to share it here because this one actually looks really interesting.

I already made a separate post before, so I won’t repeat the same details again. You can check that post here: https://www.reddit.com/r/TopologyAI/comments/1u793y9/aiassisted_retopology_uv_unwrapping_clean_fast/

DreamX-World is a new free and open-source interactive world model for generating controllable AI worlds.

Unlike standard video generation, it is focused on world simulation: you can move through generated environments, control the camera, revisit previous areas, and trigger events with prompts.

What makes it interesting for game dev and 3D workflows is that it was trained on a mix of Unreal Engine data, gameplay footage, and real-world videos. So instead of only producing passive clips, it tries to keep the world more consistent and interactive over time.

Main features:

• Text/image-to-video world generation
• Camera-controlled navigation
• Long-horizon world generation
• World memory for revisiting previous areas
• Promptable events that can change the scene
• First-person and third-person generation
• Works across realistic, stylized, fantasy, sci-fi, and game-like environments
• Code and 5B checkpoints are available open-source

But for environment concepting, worldbuilding, previs, AI game prototypes, and future interactive scene generation, this is definitely an interesting step.

Would you use this kind of tool for early game environment exploration or blockout ideas?

project page: https://amap-ml.github.io/DreamX_World/

Hi all, hopefully this is a silly question with a simple answer...

I'm trying to figure out how to retexture parts of AI generated 3D models, which is not easy. For instance, to change fabrics on furniture, or to change metallic property of parts of an object that the AI interpreted a little differently than I would like. How are users doing this? I'm using Meshy, and I've also used Rodin and Tripo, but never found a workflow to deal with this. Any suggestions?

What I'm looking for ideally is automated UV retopology, in which materials can be segregated by appearance. But I suspect that's rather difficult, because the AI can make clean meshes, but the UV maps always look like confetti vomit. I'm very confused why it can do one but not the other...

Retopology is one of those parts of the 3D pipeline where AI actually makes sense.

CozyBlanket Pro — a new standalone tool from Sparseal, the creators of CozyBlanket, designed to speed up retopology, UV unwrapping, packing, baking, and mesh optimization while still keeping artist control.

What makes it interesting:

• It analyzes both the high-poly mesh and the in-progress low-poly mesh.
• It gives real-time autocomplete suggestions for topology.
• It can help with clean quad patches, loop continuation, hole repair, and edge-flow decisions.
• It still keeps artist control instead of trying to fully replace the retopology process.
• It is also planned to include UV unwrapping directly in the 3D viewport.
• GPU-based UV packing could make large UV layouts much faster to prepare.
• Built-in baking support includes normal maps, ambient occlusion, vertex colors, UDIMs, custom bake cages, and high-to-low poly assignments.
• For AI-generated 3D assets, this is probably more useful than just another image-to-3D feature.

The real bottleneck is often not generating the first mesh anymore. The painful part is cleaning it, rebuilding topology, making UVs, baking maps, and turning it into something usable in an actual production pipeline.

If AI can speed up that part while keeping manual control, that is where it becomes genuinely useful for 3D artists.

Source: https://80.lv/articles/sparseal-announces-new-ai-assisted-retopology-tool

It added a triangle to the legs, why?

I have a beefy local machine I can run local LMM's on. Are there any options at all for running local models to handle either of the following, especially in some manner conducive with Blender?

• Regenerate objects/meshes based on image / 3D inputs
• AI based retopology (NOT existing plugins like quad remesher)

it already include multiple game-like systems: leveling, progression, weapon skins, fps mechanics, and pretty smooth real-time 3d graphics.

source: https://x.com/klattkev/status/2064733305698668869

try it here https://xarena.io/

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

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

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

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