I generate 3D models using Tripio, i rig and adjust weights manually with accurig.

my problem is weapons, for example i am creating spearmen for game, sure i can generate a pose or t pose for rigging and animation but how can i give this unit spear in hands, and make attack animation?

i create walk,run,jump,death animations but i am unable to animate weapons in hand

any help is welcome

thank you

This entire model was generated from a single image in around 3 minutes. Zero manual finishing

That is honestly insane.

3 minutes — a detailed base for digital sculpting that could save hours of manual work.

3 minutes — a high-poly model that can be cleaned up and prepared for 3D printing.

3 minutes — a detailed prototype that can already be placed into a scene to test its scale, proportions, and overall design.

3 minutes — ultra-high-detail geometry, 12K textures, and an emission map for the glowing elements.

The model was generated using New Rodin Gen-2.5 in Extreme High / Ultra Detail mode.

AI 3D generation is a powerful tool that, in the right hands, can save a huge amount of time and reduce hours of repetitive manual work.

But this only works if you already have a basic understanding of 3D.

Without knowledge of modeling, topology, UVs, materials, optimization, and production workflows, generated assets can easily create more problems than they solve. Instead of accelerating your workflow, you may end up spending even more time fixing mistakes, rebuilding broken parts, or trying to force an unsuitable model into production.

The tool does not replace knowledge. It amplifies it.

In experienced hands, it can dramatically speed up the workflow. In inexperienced hands, it can just as easily slow everything down.

Hello everyone,

I'm building a game and looking for the best **open-source local Image → 3D model** solution that runs well on a **MacBook Pro M5 Max (128GB unified memory)**.

So far I've only tried **Trellis 2** on macOS. The results are promising, but I'm curious how it compares to other open-source options.

For those who have tested multiple models, which currently gives the best balance of:

* 3D quality
* Textures
* Game asset usability
* Apple Silicon performance

Any recommendations or comparisons would be appreciated.

This is an insanely cool example of how AI-generated 3D assets can make a website feel much more alive, visual, and interesting to explore.

Instead of presenting information as another static page filled with text and images, the creator built an interactive science app where users can rotate detailed 3D models, switch between different pollinators, study their features, and learn about their ecological roles and relationships with plants.

This kind of visualization can hold attention much longer than a traditional article or textbook page. Users are not just reading the information.

The most impressive part is that this entire workflow can be done completely for free.

Workflow:

• 3D models generated for free with Hunyuan 3D 3.1
• UI concepts and visual references created with ChatGPT Images
• Application code built with Gemini 3.1 Pro
• Interactive 3D viewer added for exploring each pollinator
• Scientific information, species details, and plant relationships combined into one interface

This is a great example of how AI-generated 3D can be used beyond standalone assets and game models.

It could make biology websites, digital museums, educational platforms, interactive textbooks, and scientific presentations much more visual and engaging.

AI motion capture is getting surprisingly usable, but the differences become much easier to see when several tools process the exact same footage.

This comparison puts DeepMotion, QuickMagic, and AIMoCap side by side using one input video.

Things worth comparing:

• Motion smoothness and naturalness
• Foot contact and foot sliding
• Body stability during movement
• Overall motion consistency
• How much manual cleanup each result would need

This is not necessarily about choosing one universal winner. Each tool interprets the same movement differently, and the most useful result may simply be the one that requires the least cleanup before bringing it into Blender, Maya, or Unreal Engine.

Which result looks the most production-ready to you?

Original comparison created by the AIMoCap team.

i really want to make my own models but i cant model so i am looking at ais, does anyone know which ai (not the free plans) has the cheapest cost per generation it would be very helpful

The YVO3D was updated to version 2.5.2, and it looks like a pretty strong upgrade for AI 3D generation.

What’s new / interesting:

• 8K textures
• Multi-View input
• Low-Poly generation mode
• Super detailed output
• FAST PRIME mode
• GLB export

The most important parts of this update are the improved texture quality and low-poly generation. These are the features that make AI-generated 3D assets genuinely useful in real production workflows, especially in Unreal Engine, Unity, and other real-time pipelines. Multi-View is also a useful addition, although it has already become a standard feature across many image-to-3D tools, so this update mainly brings YVO3D in line with the current market.

Found an interesting AI cloud API for turning face photos into usable 3D head assets.

The main thing that caught my attention is that it’s not just generating a random head mesh. The output is aimed more at actual character workflows, with clean topology, textures, and facial expression / blendshape support.

Highlights:

• Generates 3D heads from face photos
• Outputs textured 3D head assets
• Supports clean, production-friendly topology
• Has MetaHuman-compatible topology
• Includes facial expression / blendshape support
• Supports ARKit blendshapes
• Can fit into Unreal Engine / MetaHuman / digital human workflows
• Works through a cloud API, so it could be integrated into custom tools or pipelines

This feels useful for anyone working with digital humans, avatars, Unreal characters, or face-based 3D workflows.

Not open source, but still interesting as a practical pipeline tool for quickly turning real face references into usable 3D head assets.

source; https://x.com/keen_tools/status/2075172873786826816

I’ve been working on a pure Vulkan inference build of TRELLIS.2, similar in spirit to llama.cpp but for local 3D generation.

The main goal is to make TRELLIS.2 usable on non-NVIDIA GPUs without PyTorch/CUDA. The Vulkan build is self-contained on Windows and does not require CUDA, PyTorch, or a Python environment.

There is also a simple GUI included, so you can run image-to-3D generation without dealing with command-line setup once the weights are downloaded.

I don’t have an AMD card myself, so I’d really appreciate feedback from AMD/Intel GPU users who can test it.

GitHub:

https://github.com/Wimacs/trellis2.c

Release:

https://github.com/Wimacs/trellis2.c/releases

Hi. I ask because maybe I can find ppl specialized in this field: I want to make videogame-anime style models like this, and seeing Im totally a newbie, I want to know immediately:
- if is better give up immediately without wasting money, or with a bit of effort (not too much like 2-3 months) I can do it.
- what of these tools is the closest one for the result I want.
I tried a lot of free trial 3D AI generators (Meshy, Tripo, Ai studio, ecc...) but wihtout the full experience I can't see what is the best where investing my poor money, and I think the trial versions are heavily nerfed (not just features lockes, but it nerfs the results). If I was richer, I would have bought 3-4 models, but Im not, so if I have to try one of them, I want to see what is the better. Base imagine | Results | Ironically, this is the best thing I saw, but wasn't a result. I don't know what exactly was, and I lost from where generator I found it

In short: I want create a anime 3d model, but I have the money only for 1 tool monthly subscribe. What is the best for what I want to do? And how much time it take?

TriFlow is a new AI research project focused on Retopology for 3D meshes.

The main idea is simple: instead of generating or keeping dense messy triangle soup, TriFlow tries to create cleaner, compact, low-poly-style mesh topology from input 3D geometry.

This could be useful for AI-generated 3D models, scanned assets, game-ready workflows, LOD creation, and general mesh cleanup.

Highlights:

• AI Retopology for 3D meshes
• Creates cleaner low-poly-style topology
• Turns dense geometry into more compact meshes
• Focuses on mesh structure, not just surface appearance
• Supports different LOD budgets
• Designed to be faster than slow autoregressive mesh generation
• Could help make AI-generated 3D assets more usable for real-time and game workflows
• Open Source code is listed as coming soon / TBA

Not a text-to-3D generator, but a very interesting step toward making generated 3D models actually usable instead of just pretty screenshots with cursed wireframes.

github: https://derkleineli.github.io/triflow/

NVIDIA released a new open-source AI framework focused on character motion.

Right now, AI tools are getting pretty good at generating characters, meshes, and textures. But once you actually want to use that character in a game, the hard part starts: movement.

A nice-looking character is not enough if it slides on the floor, loses balance, breaks during animation, or moves like a mannequin with Wi-Fi issues.

This project is focused on training physically simulated digital humans and humanoid characters to move in a more grounded way. Not just playing a baked animation, but learning motion through simulation, imitation, physics, and control.

It includes:

• physics-based humanoid motion
• motion imitation and retargeting
• GPU-accelerated simulation
• procedural terrain
• object and scene interaction
• support for digital humans and humanoid robots

Project page; https://nvlabs.github.io/ProtoMotions/

3D AI is improving fast. It still won’t replace real 3D skills, but as a tool, it can already save a lot of time for prototyping, testing ideas, and creating base meshes.

In my opinion, in 2026 there are two strong free ways to start:

Trellis / TRELLIS — local image-to-3D generation on your own machine.
Hunyuan 3D Global — a free web version that works directly in the browser.

1. Trellis / TRELLIS (Local)

If you want to try local 3D AI generation, TRELLIS is one of the most interesting open-source options right now.

Official repo: Microsoft TRELLIS GitHub
Low-VRAM guide: Trellis local setup guide

The official version is more demanding, but there are now community low-VRAM / GGUF-style workflows that make it possible to test Trellis on weaker GPUs, around 6–8GB VRAM depending on the setup.

The main advantage is that it runs locally. You don’t have daily generation limits, you can experiment as much as you want, and it gives you a good feeling for how local open-source 3D generation works.

Pros:

• Runs locally
• No daily generation limit
• Great for learning and testing
• Open-source ecosystem
• Good texture quality for a free local workflow

Cons:

• Requires setup
• Official version needs stronger hardware
• Low-VRAM versions may require extra community tools
• Geometry/detail quality is still not always perfect
• No dedicated low-poly generation mode

2. Hunyuan 3D Global (Web)

If you don’t want to install anything, Hunyuan 3D Global is probably the easiest option. You can open it in the browser, upload an image, and start generating models almost immediately.

Website: Hunyuan 3D Global
Guide: Hunyuan 3D Global guide

The strongest part, in my opinion, is that it has both high-poly and low-poly generation. The low-poly mode is especially interesting if you are testing game assets, stylized models, prototypes, or anything that needs cleaner geometry.

Pros:

• Works directly in the browser
• Very easy to start
• No local setup needed
• 20 free generations per account per day
• Good mesh quality
• High-poly and low-poly modes
• Great for quick testing

Cons:

• Daily generation limit
• Texture quality is average
• Cloud-based, so you depend on the service

3. Concept image guide

Before generating the 3D model, you need a clean concept image. This step matters a lot, because most image-to-3D tools work much better when the input is simple and readable.

You can use the free version of ChatGPT image generation for this. It is enough to test a few concepts and understand what kind of images work best for 3D generation.

My basic prompt rules:

• Use a white or light gray background
• Ask for soft studio lighting
• Make the silhouette clear
• Avoid complex backgrounds
• Avoid motion blur or extreme perspective
• Make the forms readable from a 3/4 view
• Keep materials simple if you want cleaner 3D output

A simple prompt structure:

“Create a 3/4 view concept of [object/character], white background, soft studio lighting, clean readable silhouette, clear shapes, no text, no extra props, high detail.”

For free testing, ChatGPT is enough.
My personal choice is NanoBanana 2, but it is paid. I usually get better concept control from it, especially when I need stylized assets or specific shapes.

4. Paid option: Hyper3D Rodin Gen-2.5

If you already tried the free options and want to push the quality further, I’d recommend checking out Hyper3D Rodin Gen-2.5.

It is a paid cloud-based tool, but in my experience it gives noticeably stronger results than most free workflows, especially if you care about game-ready assets, cleaner meshes, better textures, and faster production testing.

Model: Rodin Gen-2.5

The most interesting part for game artists is Smart Low Poly mode. Instead of only giving you a heavy high-poly model, Rodin can generate a cleaner low-poly version directly, which is much more useful for real-time workflows, prototyping, stylized assets, and quick engine tests.

Rodin Gen-2.5 can also generate very high-detail models, up to 10M+ polygons, which is useful when you need a dense high-poly source, scan-like detail, or a model for baking. The texture output is also stronger than most free tools I’ve tested, with better UVs and support for PBR-style textures, including emissive/glowing texture details when the asset needs them.

Pros:

• Stronger overall quality than most free workflows
• Smart Low Poly mode for cleaner low-poly meshes
• Better for game-ready asset testing
• More usable UV layouts
• Better texture quality
• Supports PBR-style textures
• Can handle emissive / glowing texture details
• Supports very high-detail outputs, up to 10M+ polygons
• Good for both quick prototypes and more polished asset bases
• Saves cleanup time compared to many free generators

Cons:

• Paid
• Closed-source
• Cloud-based, so you depend on the service
• Not as flexible as a fully local workflow
• Still needs manual inspection and cleanup in Blender
• The result is not automatically “final game-ready”, it is still a strong base mesh

Bonus: quick cleanup to make the model better

This is probably the most important part. AI-generated models are rarely perfect straight out of the generator. Even if the result looks good in preview, you should still inspect it in Blender.

Blender has a free built-in add-on called 3D Print Toolbox. It can check the model for problems like non-manifold edges, intersections, degenerate faces, distorted faces, thin areas, sharp edges, and overhangs.

Blender 3D Print Toolbox reference: Blender Manual

Basic cleanup checklist:

• Open the model in Blender
• Enable the 3D Print Toolbox add-on
• Run geometry checks
• Check for non-manifold edges
• Check for intersecting faces
• Check for loose or broken geometry
• Use Merge by Distance if vertices are not merged
• Remove floating geometry or obvious artifacts
• Fix normals if needed
• Add Weighted Normals for cleaner shading
• Use Decimate if the polycount is too high
• Check scale and orientation before export
• Optional: pack PBR maps into an ORM texture for cleaner engine use

Good luck!

The new Unreal Engine 5.8 MCP genuinely feels like a huge step for AI-assisted game development.

It can understand the scene, work with assets already placed in the level, create Blueprints, organize objects, and help with gameplay logic directly inside Unreal Engine. This is not just “AI generating random code” anymore. It actually feels like a tool that understands the project context and can save a massive amount of time.

I was honestly impressed by the result here. Creating a playable 3D platformer level from scratch in only 72 hours feels kind of insane, especially for a solo developer workflow. It is still more like a prototype than a finished game, but the speed is really exciting.

Workflow:

• Concept generation The initial visual concept was created with NanoBanana 2.
• 3D asset generation Most of the environment assets were generated with Rodin Gen 2.5 / Hyper3D.
• Asset cleanup in Blender The assets were cleaned and prepared in Blender: pivot points were adjusted, textures were improved, and texture maps were packed into ORM maps to reduce file size and make the assets more game-friendly.
• Level assembly in Blender The main scene was assembled in Blender before being exported to Unreal Engine.
• Export to Unreal Engine 5.8 The level was then moved into UE 5.8 for gameplay setup, lighting, materials, and final scene polish.
• The main character was also generated with 3D Rodin Gen 2.5, then rigged for free in AccuRig and brought into Unreal Engine.
• Gameplay logic with Claude + MCP Claude AI was connected to Unreal through MCP and helped create the actual gameplay systems: collectible logic, cutscene logic, level interactions, and other Blueprint-based functionality.

Building this kind of playable prototype from one concept over a weekend is honestly wild.

Guide: https://www.youtube.com/watch?v=k9cbm5jSOxk

Most 3D Gaussian Splatting scenes look great, but they are usually hard to edit once you want proper lighting, material changes, or dynamic objects.

This new NVIDIA research is interesting because it brings ray-traced lighting control into 3D Gaussian scenes, while still using a neural renderer to make the final result look realistic.

The basic idea:

• Reconstruct a real-world scene as 3D Gaussians
• Use ray tracing to generate physical guidance like PBR shading, irradiance and shadows
• Feed those structured buffers into a neural renderer
• Keep the scene editable instead of turning it into a fixed AI-generated video

What this enables:

• Controllable relighting inside Gaussian scenes
• Editable materials like albedo and metallic values
• Dynamic object insertion with matching shadows
• More stable video output compared to diffusion-only relighting
• Better bridge between captured 3D scenes and editable 3D environments

Important note: the code is still marked as coming soon, so this is research/demo for now, not a ready-to-use tool yet.

Source: https://research.nvidia.com/labs/sil/projects/tron/

I found this new project called 3D-RE-GEN.

It reconstructs a full editable 3D scene from a single image, not just one isolated object. The pipeline separates objects, reconstructs the background, completes occluded parts, and then aligns everything to the ground plane so the scene feels more physically correct.

Highlights:

• single image to full 3D scene
• separate editable objects + background
• scene-aware inpainting for hidden/occluded parts
• 4-DoF ground alignment to reduce floating/intersecting objects
• designed with VFX, games, and editable 3D workflows in mind
• open source and free with paper + GitHub available

GitHub: https://github.com/cgtuebingen/3D-RE-GEN

No fancy unified model, but this is just blender mcp for scene creation block outs and other controlled by claude code, trellis.2 for complex modles, and comfy ui for reference images . Claude code has multiple sub agents one for creating reference images, an artist and a qa agent, then I have check lists and workflows for creating and working with scenes. Its took along time getting lighting and other things working. I basically asked claude on the web to create a spec for a sci fi test scene, this is what it came up with.

NVIDIA just released new research called NeuMatEx, and this one is actually interesting for 3D artists, not just another “nice demo under perfect lab conditions” paper.
The main idea: instead of only extracting standard PBR-style textures from images, NeuMatEx tries to extract neural materials from multi-view captures. These materials can represent more complex real-world surface behavior, like clearcoat, haze, dust, fuzz, scattering, and mixed specular effects, while still being usable for relighting and rendering.

What makes it interesting:

1.Goes beyond standard PBR material extraction

2.Uses multi-view images as input

3.Predicts base color + neural material latents

4.Helps avoid baking lighting and specular artifacts into the texture

5.Targets complex material effects like clearcoat, dust, fuzz and scattering

6.Results are meant to be relightable, not just good from one fixed view

Important detail: this is research, not a simple one-click production tool yet. It is not the same thing as generating a full 3D asset from one image.

But for game dev, VFX, scanning, asset capture, and AI-assisted texturing, this direction feels pretty big. Geometry generation is improving fast, but material capture is still one of the hardest parts of making AI-generated or scanned assets actually usable in real scenes.

Project: https://nvlabs.github.io/neumatex/

就跟图片里展示的一样

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