Saw this workflow and thought it was a pretty interesting example of where AI-assisted character creation is right now.

The creator went from basically nothing to a fully rigged and animated character in around 6 hours using:

• ChatGPT / GPT Image for the initial character design and reference views
• Tripo AI to generate the head and body separately
• Smart topology with around 20K polys allocated to each part
• Blender to merge and clean everything up
• Manual skinning / weight painting
• Texture painting to fix the face
• Shape keys for blink, wink, smile and mouth open
• Some basic jiggle physics

Author said roughly 80% of the work was still manual Blender work, especially rigging and weight painting.

One part that seems to have saved a lot of time here was Tripo AI P2 Smart Mesh. The generated head and body came out with a much cleaner and more usable mesh than you usually expect from AI generation, so there was less time spent fighting the geometry before moving into Blender. The topology was easy enough to edit, clean up and continue working with, which made it much faster to get from generation to an actually usable character.

There are still some obvious problems too. UVs and textures can be messy, close-up quality isn't really there yet, and generated meshes still need cleanup.

post https://x.com/Dstudio_ai/status/2089209243207680484

Tripo P2.0 Preview is finally live, so everyone can test it now.

The biggest change for me is that it can now generate native quad topology while letting you choose the target polycount.

And it doesn't feel like simply taking a dense AI mesh and decimating it afterward. The polygon distribution is much more intentional: simpler surfaces stay simple, while areas that actually need the geometry get more of it.

Another thing I really like is how logically some models are structured. Instead of everything becoming one giant welded blob, separate elements can come out as actual separate parts, which makes editing the result in Blender much less painful.

Main highlights:

• Native quad topology with much cleaner polygon flow
• Logical edge loops around important shapes and details
• Models can be logically separated into individual parts, instead of everything being fused into one mesh
• Custom polycount control, so you can choose how low-poly or detailed the result should be
• Support for both quad and triangle meshes
• Better polygon distribution, with more geometry where detail is needed and fewer polygons on simple surfaces
• The mesh generation itself takes only around 5–10 seconds

Source; https://www.reddit.com/r/Tripo_ai/comments/1vrxppz/we_just_shipped_tripo_p20_preview_it_is_a_major/

We focused on generating 3D assets that go directly into your production pipeline, built for games and real-time pipelines.

Collection

What is new:

• ✨ Native Quad Topology
  • It can directly generate a clean quad mesh with more natural edge flow, making the asset easier to edit, rig, and animate.
• 📐 Wider range of complex assets
  •  Triangle topology supports 500 to 50,000 faces, while quad topology supports 500 to 25,000 faces, covering most props and hero characters.

Tripo AI's P2.0 is well-suited for the following types of workflows:

• Game characters & props: Quad topology can be go straight to rigging and animation.

Character

• Hard-surface assets: Models such as mechanical structures, weapons, vehicles, and architectural props benefit significantly from P2.0’s topology advantages. Cleaner surfaces, higher density in detailed areas, and more stable edges.

Hard Surface

• High-volume asset pipelines: Batch-generating assets while controlling the polycount, making it better suited for large-scale production in games and real-time projects.

Transparency & Scene

The official release will further improve mesh stability and add more control features.

Steps

Every user now gets 2 free generations, and Tripo P1.0 is also available to all users.

I Compared 3 AI Retopology Tools: Tripo vs Rodin vs Free Hunyuan3D

I wanted to see how current AI retopology tools handle something more complicated than a basic character.

For the test I used the same character with a mix of different shapes: organic parts, clothing, a backpack, staff and some more hard-surface-like elements.

Same source model and the same general conditions for all three.

Final mesh:

• Rodin: 35K faces
• Tripo: 46K faces
• Hunyuan3D: 66K faces

Polygon count

🥇 Rodin — 35K
Rodin was the most aggressive with optimization. It managed to simplify a lot of areas while still keeping the character recognizable and most important shapes intact.

🥈 Tripo — 46K
Tripo kept noticeably more geometry than Rodin, but a lot of those extra polygons seem to be used more intentionally around important shapes and transitions.

🥉 Hunyuan3D — 66K
Hunyuan preserved a huge amount of the original geometry. That's good for detail preservation, but not so good if your main goal is actually reducing the model.

Shape & detail preservation

🥇 Hunyuan3D
This was probably Hunyuan's strongest point. It tries to preserve almost every shape and small element from the source model.

The downside is that it doesn't really decide what needs to stay geometry. Details that could easily be represented with a normal map or texture often remain fully modeled.

🥈 Tripo
Tripo found a pretty good middle ground. Most important forms survived, while some unnecessary smaller details were simplified.

It loses a little more compared to Hunyuan, but the result feels more optimized rather than simply copied.

🥉 Rodin
Rodin simplifies the model much more aggressively. Major silhouettes and important forms are still there, but smaller shapes and secondary details can get noticeably reduced.

That's partly why it managed to reach the lowest polycount.

Topology quality

🥇 Tripo
This was the strongest result for me.

The topology feels much more intentional. Different elements are logically separated and the edge distribution generally makes more sense around the actual forms.

Out of the three, this was the closest to something I would expect from a manually planned retopology workflow.

🥈 Rodin
Rodin's topology is surprisingly decent considering how aggressively it reduces the model.

The main problem is that some areas still feel like one continuous remesh rather than topology designed specifically around individual parts.

Still, it's relatively clean and very usable for an automatic result.

🥉 Hunyuan3D
Hunyuan feels much closer to a traditional quad remesh.

It follows the source surface very closely, but doesn't seem to make many decisions about where geometry could be simplified or where topology should be structured differently.

Good surface preservation, weaker actual optimization.

Generation time

🥇 Tripo — ~1 min
Very fast. For iteration this is probably the biggest advantage because you can test multiple versions without waiting much.

🥈 Rodin — ~3 min
Still fast enough for normal production use. Slightly slower than Tripo, but considering the lower final polycount, the result is pretty reasonable.

🥉 Hunyuan3D — ~5–10 min
Definitely the slowest in my tests. Not terrible, especially considering it's free, but it becomes noticeable when you're testing multiple models.

Price

🥇 Hunyuan3D — Free
This is obviously its biggest advantage.

You can get a fully retopologized quad mesh without paying anything, which makes the result pretty impressive despite its weaknesses.

🥈 Rodin
Rodin sits somewhere in the middle for me. You pay for the generation itself, but the result is generally predictable and already fairly optimized.

🥉 Tripo
Tripo gave me the best topology, but it can become the most expensive when experimenting.

You're effectively spending credits on attempts, so if you need several generations to get the result you want, the cost starts adding up.

UVs

🥇 Rodin
Rodin produced the cleanest UV layout in this test.

The islands looked relatively organized and usable without immediately feeling like they needed to be completely redone.

🥈 Tripo
Tripo's UVs were still usable, but not as clean or organized as Rodin's.

For quick production they would probably be fine, but I would still prefer Rodin here.

🥉 Hunyuan3D
The UV result was the weakest of the three.

It works, but just like the topology itself, it feels more automatically generated and would probably need more cleanup for a serious production asset.

So for me:

Paid: Rodin 🥇
Free: Hunyuan3D 🥇

Tripo P2 is now in beta, and this might already be one of the biggest AI 3D updates of the year.

It finally generates real quad-based low-poly meshes, and you can choose the target polycount yourself. But the crazy part is that it doesn’t simply decimate a dense model — the geometry is distributed like an artist would actually build it:

• flat surfaces use fewer polygons
• detailed areas get more density
• the mesh stays within the budget you set
• everything is split into logical, editable parts instead of one welded AI blob

Hard-surface is where the difference becomes ridiculous. Flat panels actually stay clean, edge flow follows the design, and the usual melted seams, random triangles and wasted geometry are massively reduced.

The mesh generates in around 10 seconds, with PBR texturing available in the same workflow.

This is the first time AI-generated low-poly models have started to feel intentionally modeled rather than automatically simplified. If P2 holds up across more tests, this could be a massive shift for game-ready 3D generation.

Top3D.AI — compare all major 3D AI generators side by side across high-poly, low-poly, PBR, segmentation and other modes, using the same 140+ prompts and reference images under identical conditions

I wanted to test Tripo AI’s segmentation feature with an actual 3D printing use case, so I used our existing 3D Printing Guardian figurine.

I initially segmented the model into seven parts, merged them into three larger printable sections, cleaned the open surfaces in Blender, and printed each section separately on the Snapmaker U1.

That let me use different filament combinations for each print instead of being limited to four colors across the whole model. The finished figurine uses 12 colors plus 2 mixed shades.

I kept the model fairly small to make the experiment quicker, so the mixed-color transitions would probably look better on a larger version. Still, I’m very happy with how this turned out.

I documented the full workflow in my hands-on Tripo AI review on 3DWithUs, including image-to-3D, text-to-3D, Refine, segmentation, Blender cleanup, and the final physical prints:

https://3dwithus.com/tripo-ai-review-3d-printing

The two images show the separately printed sections and the final assembled result.

I’ve pretty much given up on using Codex for 3D character work. After messing with it for over two weeks, the result I got was still rough enough that I just had to admit this kind of thing really does come down to using the right tool for the job. Funny enough, image generation is still the complete opposite for me. Image models are consistently great at anime-style characters, and the aesthetics are usually there right away even when the 3D side falls apart.

From what I’ve tried so far, Tripo AI performs surprisingly well as an image-to-3D tool. It can produce recognizable anime character models that are already good enough for static display pieces, fan merch, or concept mockups. Clothing structure is still inconsistent, though, especially with skirts, which can sometimes come out more like solid cones instead of preserving the layered construction underneath.

That said, they still share the same core weakness as most AI 3D model generators right now: they can match the silhouette, but they still don’t reliably understand the fine structure of how the character is actually built. The model may look right from the front, but once you inspect the topology or think about rigging, the problems become obvious. Parts that should be separate get fused together, the hands and clothing can end up treated as one unit, and you start seeing structural errors that make the mesh unusable for animation. You also get weird cases where hair seems to grow out of the arm area, or the whole setup technically rotates but clearly isn’t built with proper character logic in mind.

So my current take is that specialized tools like Tripo or Hunyuan are already genuinely good for static 3D output, especially if your goal is a display model rather than a production-ready asset. But for actual game character pipelines, I still don’t think AI can generate something clean enough to use straight out of the box. It can get you the shape and the vibe, but not the underlying structure you need for deformation, rigging, and reliable animation.

Layer-by-layer generation does seem like it could improve things, especially if separate passes are used to preserve clothing logic and character part boundaries. The problem is that moderation limits make that route hard to control, and even if you get around that, consistency between generated parts is still difficult. Keeping proportions stable across pieces is almost harder than getting any one piece to look good on its own. If anyone has found a workflow that makes multi-part anime character generation more usable, I’d love to hear it.

This is a pretty solid example of AI being used for something beyond yet another shiny character turntable.

The developer created a full interactive human anatomy app using:

• GPT Image for the original design and references
• Tripo AI to convert each image into a 3D model
• Three.js for the web-based 3D viewer
• Codex to build the interface, interactions, illustrations and hotspot system

The first version contained almost 900 MB of 3D assets and ran at around 16 FPS. After several optimization passes, the models were reduced to roughly 2–5.5 MB each, bringing the entire asset package down to only 28.6 MB, with models loaded on demand.

Users can rotate and inspect, view where they sit inside the body, open educational illustrations and interact with hotspots explaining different anatomical areas.

Not a one-click workflow, obviously, because reality continues refusing to be that convenient, but it shows how image generation, AI 3D tools and coding agents can be combined into a genuinely useful educational product.