DEV Community: Alejandro iopjg

Building an Image-to-3D Workflow with Pixal3D: From One Image to a GLB Asset

Alejandro iopjg — Wed, 27 May 2026 02:07:19 +0000

Image-to-3D has been one of those AI categories that looks magical in demos but becomes much harder when you try to turn it into a real user-facing product.

The demo is simple:

Upload an image
Wait for the model
Download a 3D asset

But in a real workflow, users ask very different questions:

Will the model preserve the shape of my object?
Can I preview it before downloading?
Is the output compatible with Blender, Unity, Unreal, or three.js?
What if one model works better for shoes, and another works better for toys or furniture?
Can I compare different AI 3D models without learning every API?

That is why Pixal3D is interesting.

Pixal3D is a new image-to-3D model focused on pixel-aligned 3D generation. In simple terms, it tries to preserve a stronger relationship between the original 2D image and the generated 3D asset. For developers building 3D tools, this matters because users usually do not judge the output only by whether it is “3D.” They judge it by whether it still feels like the object they uploaded.

Why Pixal3D caught my attention

Most image-to-3D tools already promise the same basic result: upload a picture and get a 3D model.

The problem is fidelity.

A generated model may look clean, but the proportions may drift. A product may lose important details. A character may look close from one angle but strange when rotated. A sneaker may look like a sneaker, but not like that sneaker.

Pixal3D’s core idea is useful because it focuses on the relationship between image pixels and 3D structure. Instead of treating the image mostly as a loose visual condition, Pixal3D is designed around stronger pixel-to-3D alignment.

For a developer, that shifts the product conversation from:

“Can AI generate a 3D model?”

to:

“Can AI generate a 3D model that still respects the input image?”

That is a much more useful question.

What the basic workflow looks like

A simple Pixal3D-style product workflow can look like this:

User uploads image
↓
Store image or convert it to a public URL
↓
Send image URL to Pixal3D API
↓
Poll or wait for generation result
↓
Receive GLB model
↓
Render GLB preview in browser
↓
Allow download or further editing

The GLB output is important because it works well for web-based 3D preview and downstream workflows. In a browser product, you can preview the generated model with three.js or React Three Fiber instead of forcing the user to download blindly.

Why preview matters

For image-to-3D products, preview is not a small UI feature. It is part of the product value.

Users need to rotate the model.

They need to inspect the back side.

They need to see whether the texture is acceptable.

They need to decide whether the result is good enough before spending more credits or downloading the asset.

A good product should not just expose the model API. It should make the AI output understandable.

That means:

Show a real-time 3D viewer
Allow rotate, zoom, and pan
Provide model size and format information
Make the download button obvious
Let users compare different model outputs when possible

This is also why I believe image-to-3D products should support multiple models over time. Pixal3D may be strong for fidelity, but another model might be faster, cheaper, or better for certain object types.

Multi-model image-to-3D is probably the better product layer

As developers, we often think the model is the product.

But for end users, the product is the workflow.

A user does not really want “Pixal3D API access.” They want:

a fast way to turn an image into a 3D model
a clean viewer
a reliable download
a model that works for their object type
less trial and error

That is the direction I am taking with AI Image to 3D. Instead of treating every new model as a separate tool, I think the better user experience is to provide a single place where users can test different image-to-3D models and pick the best result.

Pixal3D is now one of the models I am integrating into that workflow.

Practical input tips

From testing image-to-3D tools in general, the input image still matters a lot.

Better results usually come from:

a single clear object
simple background
good lighting
minimal occlusion
sharp edges
enough visible structure
front or three-quarter view

Bad inputs often create bad 3D assets no matter how strong the model is.

So a production product should guide users before generation. For example:

Good input:
A clear product image on a simple background.

Bad input:
A crowded photo with multiple overlapping objects.

This sounds basic, but it reduces failed generations and support questions.

What I would build around Pixal3D

If I were building an image-to-3D app from scratch, I would not stop at “upload image, return GLB.”

I would build:

Image upload and cleanup
Background removal option
Pixal3D generation
Browser-based GLB preview
Download in GLB
Model comparison with other 3D models
Optional AI texture workflow
Gallery of successful examples

The API is only one part of the product. The surrounding workflow is what makes it useful.

Final thoughts

Pixal3D is interesting because it focuses on one of the biggest practical problems in image-to-3D: fidelity to the input image.

For developers, this opens up a more useful product direction. Instead of only asking which model is newest, we should ask:

Which model works best for this object?
How do users evaluate the output?
How do we make 3D generation less confusing?
How do we turn raw GLB generation into a complete workflow?

That is where the opportunity is.

I am currently integrating Pixal3D into AI Image to 3D as part of a multi-model image to 3D workflow. My goal is simple: let users upload one image, test different AI 3D models, preview the result in the browser, and download the model that works best for their use case.

Why AI Video Generation Needs Motion Control, Not Just Better Prompts

Alejandro iopjg — Fri, 15 May 2026 07:55:52 +0000

AI video generation has improved quickly, but one problem still appears again and again:

motion is hard to control.

You can write a detailed prompt.

You can describe the scene, the character, the camera angle, and the mood.

But when the video is generated, the movement may still feel random.

The character might walk in the wrong direction.

The pose might change too much.

The motion may not match the action you imagined.

The result can be beautiful, but not always usable.

For many creative workflows, this is a real limitation.

Prompt-only video generation has a control problem

Text prompts are great for describing intent.

For example:

A stylish avatar walks forward on a city street, cinematic lighting, realistic motion.

This sounds clear to a human.

But for an AI video model, there are still many open questions:

How fast should the character walk?
Should the body turn?
What should the hands do?
How much camera movement is needed?
Should the pose stay consistent?
What motion rhythm should be followed?

A prompt can describe the idea, but it does not always define the motion precisely.

That is why many AI video results feel impressive at first glance, but difficult to reuse in real production workflows.

Reference-based motion is a better interface

A more controllable workflow is:

Upload a reference image
Upload a motion reference video
Generate a new AI video based on both inputs

In this workflow, the image provides the subject or character.

The motion video provides the movement.

The AI model then tries to transfer or follow the motion from the video while preserving the visual identity from the image.

This is useful because users do not need to describe every movement in words. They can simply show the motion they want.

Why this matters for creators and builders

Motion control is especially useful for cases like:

Character animation
AI avatar videos
Brand mascot videos
AI influencer clips
Social media content
Product and marketing visuals

For example, a creator may already have a character image, but wants that character to wave, walk, dance, present a product, or follow a simple action from a real video.

Instead of trying to describe the movement perfectly with text, the creator can use a short motion reference video.

This makes the workflow more visual, more predictable, and easier to repeat.

Motion control changes the role of prompts

This does not mean prompts are no longer useful.

Prompts are still helpful for describing:

Scene style
Background
Lighting
Mood
Camera direction
Small visual details

But prompts should not be the only control layer.

A better AI video workflow can combine:

Reference image for identity
Reference video for motion
Prompt for scene and style
Quality settings for output control

This is closer to how creators actually think.

They often know what a character should look like, what motion they want, and what style the final video should have.

What I built

I recently built MotionVideo AI, a motion control AI video generator focused on this workflow.

The idea is simple:

Upload a reference image and a motion reference video, then generate a motion-controlled AI video online.

The current version focuses on helping users animate characters, avatars, mascots, and AI influencers with reference-based motion.

It is still early, but the goal is to make AI video generation more controllable and less dependent on guessing the perfect prompt.

You can try it here:

MotionVideo AI

Final thought

AI video tools are not just competing on output quality anymore.

The next important layer is control.

For many users, the question is no longer only:

Can AI generate a video?

It is becoming:

Can I control the video well enough to use it?

That is why motion control feels like an important direction for AI video products.

I Built a Small AI Music Workflow: From Rap Ideas to Lyric Videos

Alejandro iopjg — Tue, 12 May 2026 12:54:58 +0000

A lot of AI products start with a broad promise:

Generate anything.

That sounds powerful, but as a developer building small AI tools, I’ve started to think that “generate anything” is often too vague.

Users usually don’t wake up thinking:

I need a general AI generation platform.

They think:

I have an idea. How do I turn it into something I can use?

For creative tools, that difference matters.

Recently I’ve been working on a small AI music workflow around this idea:

idea → lyrics → rap demo → lyric video → shareable content

This post is not a technical deep dive into model internals. It is more about product thinking, workflow design, and what I learned while building niche AI tools for creators.

Why I chose a niche workflow instead of a general AI music tool

AI music tools are getting very good.

But many of them are broad by design. They try to support every genre, every mood, every voice, every kind of song, and every type of user.

That is useful, but it can also create friction.

A broad tool often makes the user think too much before they get started:

What genre should I choose?
How should I describe the vocal style?
Should I write my own lyrics?
What should the final output be?
What do I do after the song is generated?

For a power user, that flexibility is great.

For a casual creator, it can feel like work.

So instead of building another general AI music generator, I wanted to test a narrower idea:

What if the tool focused specifically on rap?

Rap is interesting because the output is not only about melody. Flow, rhythm, delivery, rhyme, attitude, and beat all matter.

A lyric can look good on screen but feel weak when performed. Another simple line can work surprisingly well if the flow and delivery are right.

That is why a text-only lyrics tool is not always enough.
**
Step 1: From idea to rap demo**

The first part of the workflow is an AI Rap Generator

The goal is simple:

Let someone start with a topic, idea, or their own lyrics and generate a listenable rap track.

Instead of only returning lyrics, the workflow tries to create something closer to a rough demo:

lyrics
vocals
flow
beat
style or mood direction

The important product decision here is that the output should be listenable, not just readable.

That changes how users judge the result.

If you only generate lyrics, the user has to imagine the delivery. But if you generate a track, they can quickly hear whether the idea has energy.

For example, a creator might start with:

A motivational rap about building something from zero

Or:

A short rap hook for a startup launch video

Or paste their own lyrics and test how they sound as a rap song.

The result does not need to be a finished studio-quality track. At this stage, the job of the tool is to help the user answer one question:

Is this idea worth developing further?

That is a useful job.

Step 2: From audio to visual content

After generating a song idea, the next question is:

How do I share it?

This is where many AI music workflows stop too early.

A track is useful, but most creators are publishing on visual platforms:

TikTok
YouTube Shorts
Instagram Reels
X
product landing pages
launch posts

Audio alone is often not enough.

That is why the second part of the workflow is an AI Lyric Video Generator
.

The idea is to take a song or lyrics and turn them into a visual format that is easier to share.

A lyric video can add:

synchronized lyrics
visual rhythm
background scenes
abstract motion
story-style visuals
a format that works better on short-form platforms

From a product perspective, this matters because the user’s real goal is often not “generate a song.”

Their real goal is closer to:

Create something I can post.

That is a different problem.

The product lesson: outputs should connect to the next step

One mistake I see in AI tools is that they treat generation as the final step.

But for users, generation is often just one step in a larger workflow.

A developer may generate code, then test it, edit it, commit it, and deploy it.

A designer may generate images, then select, edit, resize, and publish them.

A music creator may generate a song, then make a video, post it, and see how people respond.

So when designing an AI product, I think it helps to ask:

What does the user want to do after this output is generated?

For the rap workflow, the next step is often visual content.

That is why “AI Rap Generator” and “AI Lyric Video Generator” fit together better than they might seem at first.

One creates the audio idea.

The other helps turn it into shareable content.

Why niche AI tools can feel more useful

Niche tools are not always bigger businesses, but they can be easier to understand.

A broad AI tool says:

Tell me anything you want.

A niche AI tool says:

I help you finish this specific job.

That clarity can be valuable.

For example:

“Generate music” is broad.
“Turn a rap idea into a demo” is specific.
“Create a lyric video from a song” is specific.

Specific tools reduce the user’s decision-making.

They also make positioning easier.

Instead of explaining a giant platform, you can explain one clear workflow.

That is especially helpful for small teams or solo builders.

How I think about the workflow

The workflow I’m testing looks like this:

User has a topic, idea, or lyrics
AI generates a rap demo
User reviews the lyrics, flow, vocals, and beat
User refines the idea if needed
User turns the song into a lyric video
User shares the result as content

It is simple, but that is the point.

A creator should not need to understand music production, video editing, prompt engineering, and motion design just to test one idea.

AI can reduce the friction between idea and first output.

The creator still brings the taste, judgment, and direction.

What I would improve next

If I continue building this workflow, the areas I would focus on are:

Better style controls

Rap is not one thing.

Trap, drill, boom bap, melodic rap, freestyle, and storytelling rap all feel different. Better style controls would make the output feel more intentional.

Better feedback loop

The user should be able to say:

make the hook more catchy
make the flow more aggressive
make the lyrics simpler
make it sound more emotional

A good AI creator tool should support iteration, not just one-shot generation.

Better connection between audio and video

The best experience would be a smoother handoff from generated song to lyric video.

For example:

generate song → extract lyrics → detect mood → create matching video style

That would make the whole workflow feel more complete.

Final thought

As AI tools become more powerful, I think the opportunity for developers is not only in building bigger models or broader platforms.

There is also a lot of value in building focused workflows.

Not:

Generate anything.

But:

Help this specific user complete this specific creative job.

For me, the interesting workflow is:

rap idea → listenable track → lyric video → shareable content

That is the kind of AI tool I want to keep exploring.

Show DEV: I Built an Image-to-3D SaaS Using Tencent's New Hunyuan 3D AI

Alejandro iopjg — Thu, 13 Nov 2025 07:19:17 +0000

As a developer (and indie hacker), I've always been obsessed with building tools. Lately, I’ve been fascinated by the AI space, especially the wild tech that can turn 2D images into 3D models.

For so long, 3D modeling has been locked away inside pro software like Blender or Maya, with a massive learning curve. But what if... we could make it as simple as uploading a file?

The New Toy: Tencent Hunyuan 3D
Recently, I've been keeping a close eye on the Tencent Hunyuan 3D model.

Unlike DALL-E generating images, Hunyuan 3D is hyper-focused on generating high-fidelity 3D models. What caught my eye about it was:

Multi-modal Input: It doesn't just support "single image-to-3d," it also supports "multi-view"—letting you upload front, side, and back shots of an object for a much more accurate model.

PBR Materials: It can auto-generate PBR (Physically-Based Rendering) textures, meaning the models look realistic and can be dropped right into a game engine.

High Fidelity: The docs (and my tests) show it's incredibly good at handling sharp edges and geometric structures.

When I saw this powerful model (and its available API), I immediately thought, "This is way too cool to just live in a technical paper."

The Idea: A "Homepage is the Product" Tool
I wanted to build a pure SaaS tool. The kind you land on and can immediately use (think remove.bg or tinypng).

My goal: Create an Image to 3D converter that anyone can use—whether they're a 3D printing hobbyist, a game dev, or just curious.

Introducing: My SaaS Baby, Image3D AI
So, after many late nights and countless cups of coffee, I'm super excited to show you the project:

Image3D AI (Replace with your actual domain)

It's a clean, no-fluff SaaS that puts the power of Hunyuan 3D right in your browser.

The Cool Features
This isn't just a simple API skin; I built a full workflow around it:

✨ Dual Generation Modes You can choose "Single Image" (perfect for logos, sketches) or "Multi-view" (for real-world objects, way more accurate).

⚙️ Pro Parameter Control You can select different generation modes:

Normal: Balances quality and speed.

Low Poly: Generates an optimized, low-polygon mesh (game devs love this).

Geometry: Creates a textureless "white model," perfect for 3D printing (image to stl).

🚀 Multi-Format Export (The Big One!) This might be the most important part. You can export your generated model as:

.STL (for all the 3D printing folks)

.OBJ (for traditional 3D editing)

.GLB (the future! It's the standard for Web and AR, packing all textures inside)

The Tech Stack
I know this is what you're waiting for 😉. This is a 100% Jamstack-powered project:

Frontend: Next.js (my beloved) + Tailwind CSS (using shadcn/ui)

3D Viewer: @react-three/fiber (R3F) - So much fun to work with.

Backend/API: Next.js API Routes (Serverless Functions)

Deployment: Vercel

The AI Core: The Tencent Hunyuan 3D API

Come Try It Out! (Please!)
This has been an incredible learning process for me, especially figuring out the 3D interaction and optimizing the API calls.

I would be absolutely thrilled if you'd come try it out and give me some feedback!

👉 Visit the site: https://www.aiImageTo3d.com

Try uploading your company logo.

Try taking pics of a coffee mug on your desk (using multi-view).

All feedback is welcome! What features should I build next? (I'm thinking batch processing...)

Let me know what you think in the comments below!