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Tested this concepting workflow. ▶️ Converting a level design image to a 3D asset gallery image ▶️ re-generating segmented subjects into separate high quality images. ▶️ using these images to generate the 3D meshes. First time using 'for loops' in ComfyUI, works great and the prompts are updated for...

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Jerome | InsaneUnreal

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17,461 views • 6 months ago •via X (Twitter)

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New clean 3D AI workflow Control Rig physics in UEFN! Been exploring some more hybrid AI workflows lately, and for 3D assets splitting the model into parts is kinda essential. This video shows my first test using AssetHub, one of the cleanest ways i've used to keep the whole generation process organized. It auto-detected the pose in my main image and broke it down into parts, then generated images for each. From there I can just iterate on each piece separately and generate the 3D models in one workflow. AssetHub is still in closed beta atm, so let me know if you want to try it out. Main reference image came from Grok Imagine. All the part images and high poly meshes were done in AssetHub, selected Tripo 3.1 as the main model. After that I ran it through Hunyuan 3D for retopo + unwrap. Final assembly in Blender. Baked normals from high to low poly. End result: under 60K tris, 3 materials, 2K textures. Also Control Rig physics in Unreal Engine are insane. It finally validates in UEFN too, so of course I had to test it out.
1:17

New clean 3D AI workflow Control Rig physics in UEFN! Been exploring some more hybrid AI workflows lately, and for 3D assets splitting the model into parts is kinda essential. This video shows my first test using AssetHub, one of the cleanest ways i've used to keep the whole generation process organized. It auto-detected the pose in my main image and broke it down into parts, then generated images for each. From there I can just iterate on each piece separately and generate the 3D models in one workflow. AssetHub is still in closed beta atm, so let me know if you want to try it out. Main reference image came from Grok Imagine. All the part images and high poly meshes were done in AssetHub, selected Tripo 3.1 as the main model. After that I ran it through Hunyuan 3D for retopo + unwrap. Final assembly in Blender. Baked normals from high to low poly. End result: under 60K tris, 3 materials, 2K textures. Also Control Rig physics in Unreal Engine are insane. It finally validates in UEFN too, so of course I had to test it out.

Jerome | InsaneUnreal

82,884 views • 5 months ago

This is probably the most complex workflow I’ve ever built, only with open-source tools. It took my 4 days. It takes four inputs: author, title, and style; and generates a full visual animated story in one click in ComfyUI . I worked on it for four days. There are still some bugs, but here’s the first preview. Here’s a quick breakdown: - The four inputs are sent to LLMs with precise instructions to generate: first, prompts for images and image modifications; second, prompts for animations; third, prompts for generating music. - All voices are generated from the text and timed precisely, as they determine the length of each animation segment. - The first image and video are generated to serve as the title, but also as the guide for all other images created for the video. - Titles and subtitles are also added automatically in Comfy. - I also developed a lot of custom nodes for minor frame calculations, mostly to match audio and video. - The full system is a large loop that, for each line of text, generates an image and then a video from that image. The loop was the hardest part to build in this workflow, so it can process either a 20-second video or a 2-minute video with the same input. - There are multiple combinations of LLMs that try to understand the text in the best way to provide the best prompts for images and video. - The final video is assembled entirely within ComfyUI. - The music is generated based on the LLM output and matches the exact timing of the full animation. - Done! For reference, this workflow uses a lot of models and only works on an RTX 6000 Pro with plenty of RAM. My goal is not to replace humans, as I’ll try to explain later, this workflow is highly controlled and can be adapted or reworked at any point by real artists! My aim was to create a tool that can animate text in one go, allowing the AI some freedom while keeping a strict flow. I don’t know yet how I’ll share this workflow with people, I still need to polish it properly, but maybe through Patreon. Anyway, I hope you enjoy my research, and let’s always keep pushing further! :)
0:55

This is probably the most complex workflow I’ve ever built, only with open-source tools. It took my 4 days. It takes four inputs: author, title, and style; and generates a full visual animated story in one click in ComfyUI . I worked on it for four days. There are still some bugs, but here’s the first preview. Here’s a quick breakdown: - The four inputs are sent to LLMs with precise instructions to generate: first, prompts for images and image modifications; second, prompts for animations; third, prompts for generating music. - All voices are generated from the text and timed precisely, as they determine the length of each animation segment. - The first image and video are generated to serve as the title, but also as the guide for all other images created for the video. - Titles and subtitles are also added automatically in Comfy. - I also developed a lot of custom nodes for minor frame calculations, mostly to match audio and video. - The full system is a large loop that, for each line of text, generates an image and then a video from that image. The loop was the hardest part to build in this workflow, so it can process either a 20-second video or a 2-minute video with the same input. - There are multiple combinations of LLMs that try to understand the text in the best way to provide the best prompts for images and video. - The final video is assembled entirely within ComfyUI. - The music is generated based on the LLM output and matches the exact timing of the full animation. - Done! For reference, this workflow uses a lot of models and only works on an RTX 6000 Pro with plenty of RAM. My goal is not to replace humans, as I’ll try to explain later, this workflow is highly controlled and can be adapted or reworked at any point by real artists! My aim was to create a tool that can animate text in one go, allowing the AI some freedom while keeping a strict flow. I don’t know yet how I’ll share this workflow with people, I still need to polish it properly, but maybe through Patreon. Anyway, I hope you enjoy my research, and let’s always keep pushing further! :)

Lovis Odin

58,841 views • 1 year ago

From generated reference images to riding a robot raptor in Fortnite 🦖 For now mostly the same workflow, but I also used Nano Banana to get that toe claw in the image lol. Other steps: Images from Grok Imagine, Tripo for raw 3D generation, Hunyuan 3D Studio for lowpoly and UV unwrap, Trellis2 in ComfyUI for texturing and AI upscaling. Blender for cleanup, rigging and weight painting. Splitting up the body parts helped to get a more mid-poly result. Also with better control during cleanup. The final result has 28K triangles. Again with the awesome retargeting tools in Unreal Engine / UEFN. I’ll be testing different AI texturing approaches soon. Some parts can get a bit wobbly at times, most notable in hard-surface objects. Also curious if there are any upcoming weight painting tools 😄
1:03

From generated reference images to riding a robot raptor in Fortnite 🦖 For now mostly the same workflow, but I also used Nano Banana to get that toe claw in the image lol. Other steps: Images from Grok Imagine, Tripo for raw 3D generation, Hunyuan 3D Studio for lowpoly and UV unwrap, Trellis2 in ComfyUI for texturing and AI upscaling. Blender for cleanup, rigging and weight painting. Splitting up the body parts helped to get a more mid-poly result. Also with better control during cleanup. The final result has 28K triangles. Again with the awesome retargeting tools in Unreal Engine / UEFN. I’ll be testing different AI texturing approaches soon. Some parts can get a bit wobbly at times, most notable in hard-surface objects. Also curious if there are any upcoming weight painting tools 😄

Jerome | InsaneUnreal

41,098 views • 7 months ago

Hard surface model test: 3D model of an unfinished overland build. Got really nice images again from Grok. Also realised an ‘unfinished’ vehicle might not be the best idea lol. Anyways, I was well surprised with the results. I don’t trust rotating a generated wheel yet 😁, so I made the wheels in Blender. Very similar workflow as the mech character: Images from Grok Imagine, Tripo for raw 3D generation, Hunyuan 3D Studio for lowpoly and UV unwrap, Trellis2 in ComfyUI for texturing + upscaling. Blender final preparation. Unreal Engine / UEFN shaders. Next time i'd work a bit longer on the normals.
1:07

Hard surface model test: 3D model of an unfinished overland build. Got really nice images again from Grok. Also realised an ‘unfinished’ vehicle might not be the best idea lol. Anyways, I was well surprised with the results. I don’t trust rotating a generated wheel yet 😁, so I made the wheels in Blender. Very similar workflow as the mech character: Images from Grok Imagine, Tripo for raw 3D generation, Hunyuan 3D Studio for lowpoly and UV unwrap, Trellis2 in ComfyUI for texturing + upscaling. Blender final preparation. Unreal Engine / UEFN shaders. Next time i'd work a bit longer on the normals.

Jerome | InsaneUnreal

169,087 views • 7 months ago

Here's a look behind the scenes of how I used a 3d model, generated from an image, as a "driver" for ai animation. In the near future we'll see more 3d tools that support these types of workflows, with much more emphasis on enhancing user input instead of just letting the ai do everything. I used the 3d model to generate keyframes that I then animated in Luma Dream. By using a 3d model with a diffusion "layer" done with Krea, I got quite high quality frames with a relatively high consistency as well since the AI didn't have to "hallucinate" that much. I upscaled and detailed the frames using Magnific. Using semi-detailed 3d models as drivers for gen ai is very powerful, and it allows us to use models that are sculpted in a more free-flowing type of workflow that doesn't rely so much on high surface detailing or very time-consuming finish, but instead relies more on gestural 3d sculpting. #art #ai
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Here's a look behind the scenes of how I used a 3d model, generated from an image, as a "driver" for ai animation. In the near future we'll see more 3d tools that support these types of workflows, with much more emphasis on enhancing user input instead of just letting the ai do everything. I used the 3d model to generate keyframes that I then animated in Luma Dream. By using a 3d model with a diffusion "layer" done with Krea, I got quite high quality frames with a relatively high consistency as well since the AI didn't have to "hallucinate" that much. I upscaled and detailed the frames using Magnific. Using semi-detailed 3d models as drivers for gen ai is very powerful, and it allows us to use models that are sculpted in a more free-flowing type of workflow that doesn't rely so much on high surface detailing or very time-consuming finish, but instead relies more on gestural 3d sculpting. #art #ai

Martin Nebelong

48,280 views • 2 years ago

A Web-Based, Agentic ComfyUI-Style Builder for 3D Okay, this is actually pretty interesting. I’ve been testing Flow, a new tool from 3DAIStudio . It’s basically a web-based, ComfyUI-style workflow builder made around 3D, with specialized nodes, logic nodes, and an AI agent that can help build the workflow for you. I tested it across a few different pipelines, models, and node setups — and it works surprisingly well. Full review + results in the video 👇
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A Web-Based, Agentic ComfyUI-Style Builder for 3D Okay, this is actually pretty interesting. I’ve been testing Flow, a new tool from 3DAIStudio . It’s basically a web-based, ComfyUI-style workflow builder made around 3D, with specialized nodes, logic nodes, and an AI agent that can help build the workflow for you. I tested it across a few different pipelines, models, and node setups — and it works surprisingly well. Full review + results in the video 👇

Stefan 3D AI

10,561 views • 2 months ago

The battle of the Text/Image to 3D Model 👍- To make this fair, I tested the same image across all platforms and used the first generated result. No cherry picking allowed! I also tested what I think are the 4 top services to use right now. Let me know what you think is the best 3D mesh generation from these platforms however I think it's quite obvious that Tencent Hy 2.5 is currently the king 👑 It wins in overall detail and sharpness, accuracy in relation to the input image, as well as texture generation and projection. Tripo 2.5 comes in second and Rodin 3rd with Trellis in 4th place. However, Rodin has the best topology since it can generate a mesh in quads BUT I left all of them in triangles to keep it fair. Hi3DGen can't be counted as it can't even generate textures and it has no multi-image support. Meshy was another option but the results were not great. Is there any other AI 3D Gen services that you know of that you want me to test as well?
1:45

The battle of the Text/Image to 3D Model 👍- To make this fair, I tested the same image across all platforms and used the first generated result. No cherry picking allowed! I also tested what I think are the 4 top services to use right now. Let me know what you think is the best 3D mesh generation from these platforms however I think it's quite obvious that Tencent Hy 2.5 is currently the king 👑 It wins in overall detail and sharpness, accuracy in relation to the input image, as well as texture generation and projection. Tripo 2.5 comes in second and Rodin 3rd with Trellis in 4th place. However, Rodin has the best topology since it can generate a mesh in quads BUT I left all of them in triangles to keep it fair. Hi3DGen can't be counted as it can't even generate textures and it has no multi-image support. Meshy was another option but the results were not great. Is there any other AI 3D Gen services that you know of that you want me to test as well?

Travis Davids

61,232 views • 1 year ago

"#AI will never create clean meshes" AI 👇 This is the new #AI model from NVIDIA called Meshtron (jeez what a name 😅). It generates meshes of complex 3D objects closely resembling those created by professional artists. It opens the door to a more realistic generation of detailed 3D assets for animation, gaming, and virtual environments. The current version accepts the following control inputs: - Point cloud: Determines the shape of the output mesh. - Face count: Determines the density of the output mesh. - Quad ratio: Switches between quad and triangle tessellation. - Creativity: Can be adjusted to generate extra details in the model. Meshtron can either serve as a stand-alone remesher to improve the quality of existing models, or be used in tandem with a text-to-3D or image-to-3D model to generate artist-grade meshes from scratch.
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"#AI will never create clean meshes" AI 👇 This is the new #AI model from NVIDIA called Meshtron (jeez what a name 😅). It generates meshes of complex 3D objects closely resembling those created by professional artists. It opens the door to a more realistic generation of detailed 3D assets for animation, gaming, and virtual environments. The current version accepts the following control inputs: - Point cloud: Determines the shape of the output mesh. - Face count: Determines the density of the output mesh. - Quad ratio: Switches between quad and triangle tessellation. - Creativity: Can be adjusted to generate extra details in the model. Meshtron can either serve as a stand-alone remesher to improve the quality of existing models, or be used in tandem with a text-to-3D or image-to-3D model to generate artist-grade meshes from scratch.

Gabriele Romagnoli

2,177,587 views • 1 year ago

Generating flipbooks in ComfyUI. Nothing too crazy, but these little AI workflows are definitely helpful! 🐦 When I input a black frame to the FirstLastFrame node I got some decent impulse textures. using Wan 2.2 btw. For the bird to work i had to generate between 30-60 frames and extract every 4 frames to get better wing motion. In UEFN / Unreal Engine i'm offsetting the animation phase for every particle to get a more random result.
0:59

Generating flipbooks in ComfyUI. Nothing too crazy, but these little AI workflows are definitely helpful! 🐦 When I input a black frame to the FirstLastFrame node I got some decent impulse textures. using Wan 2.2 btw. For the bird to work i had to generate between 30-60 frames and extract every 4 frames to get better wing motion. In UEFN / Unreal Engine i'm offsetting the animation phase for every particle to get a more random result.

Jerome | InsaneUnreal

10,952 views • 7 months ago

Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priors paper page: present Magic123, a two-stage coarse-to-fine approach for high-quality, textured 3D meshes generation from a single unposed image in the wild using both2D and 3D priors. In the first stage, we optimize a neural radiance field to produce a coarse geometry. In the second stage, we adopt a memory-efficient differentiable mesh representation to yield a high-resolution mesh with a visually appealing texture. In both stages, the 3D content is learned through reference view supervision and novel views guided by a combination of 2D and 3D diffusion priors. We introduce a single trade-off parameter between the 2D and 3D priors to control exploration (more imaginative) and exploitation (more precise) of the generated geometry. Additionally, we employ textual inversion and monocular depth regularization to encourage consistent appearances across views and to prevent degenerate solutions, respectively. Magic123 demonstrates a significant improvement over previous image-to-3D techniques, as validated through extensive experiments on synthetic benchmarks and diverse real-world images.
0:34

Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priors paper page: present Magic123, a two-stage coarse-to-fine approach for high-quality, textured 3D meshes generation from a single unposed image in the wild using both2D and 3D priors. In the first stage, we optimize a neural radiance field to produce a coarse geometry. In the second stage, we adopt a memory-efficient differentiable mesh representation to yield a high-resolution mesh with a visually appealing texture. In both stages, the 3D content is learned through reference view supervision and novel views guided by a combination of 2D and 3D diffusion priors. We introduce a single trade-off parameter between the 2D and 3D priors to control exploration (more imaginative) and exploitation (more precise) of the generated geometry. Additionally, we employ textual inversion and monocular depth regularization to encourage consistent appearances across views and to prevent degenerate solutions, respectively. Magic123 demonstrates a significant improvement over previous image-to-3D techniques, as validated through extensive experiments on synthetic benchmarks and diverse real-world images.

AK

305,704 views • 3 years ago

I tried to see how far I could push my current AI + 3D workflow and build a playable 3D game prototype in just 3 days😶 Not by asking AI to make the whole game in one prompt. I had my own idea, knew what I wanted to build, and used AI as a tool: - for concepting, - asset generation & texturing, - gameplay logic with the new UE 5.8 MCP + Claude Code. And honestly, it is crazy how far you can get now with the right workflow. Full video - #UE5 #ClaudeCode #AI3D #3DAI
1:02

I tried to see how far I could push my current AI + 3D workflow and build a playable 3D game prototype in just 3 days😶 Not by asking AI to make the whole game in one prompt. I had my own idea, knew what I wanted to build, and used AI as a tool: - for concepting, - asset generation & texturing, - gameplay logic with the new UE 5.8 MCP + Claude Code. And honestly, it is crazy how far you can get now with the right workflow. Full video - #UE5 #ClaudeCode #AI3D #3DAI

Stefan 3D AI

20,994 views • 2 months ago

🚨 A single image → a fully editable UE5 world. Introducing Lumera: a turnkey pipeline that reconstructs an engine-native 3D scene directly from ONE image — not just pixels, point clouds, or Gaussians. It gives you: → object-level 3D meshes → editable 3D layouts → engine-native lights → HDR environment lighting → a scene ready for Unreal Engine 5 In other words: Image → 3D world → UE5. We believe the future of image-to-3D is not “generate something that looks 3D.” It is generate the actual world project. 🌐 📄
3:52

🚨 A single image → a fully editable UE5 world. Introducing Lumera: a turnkey pipeline that reconstructs an engine-native 3D scene directly from ONE image — not just pixels, point clouds, or Gaussians. It gives you: → object-level 3D meshes → editable 3D layouts → engine-native lights → HDR environment lighting → a scene ready for Unreal Engine 5 In other words: Image → 3D world → UE5. We believe the future of image-to-3D is not “generate something that looks 3D.” It is generate the actual world project. 🌐 📄

Hao Zhao

51,795 views • 17 days ago

Using MeshyAI alongside Blender with Codex/Astra for 3D game creation is such an unlock for higher-fidelity characters (the models aren't great at procedurally creating characters in my experience) My workflow starts by generating high-res image refs (in t-pose and nothing else in background) of target characters in Codex/GPT Image 2 (or finding some online) The model then handles image-to-3d via Meshy REST API, as well as rigging + animating the models and integrating them into target game (the latter piece is where frontier models really stand out) I always have the agent create a local studio page so I can view and interact with the generated models! Tl;dr: Bowser T-pose image → 4 Blender reference images → Meshy image-to-3D → Blender cleanup (3M → 35K triangles) → Meshy auto-rigging → walk/run animations → interactive Three.js viewer
0:21

Using MeshyAI alongside Blender with Codex/Astra for 3D game creation is such an unlock for higher-fidelity characters (the models aren't great at procedurally creating characters in my experience) My workflow starts by generating high-res image refs (in t-pose and nothing else in background) of target characters in Codex/GPT Image 2 (or finding some online) The model then handles image-to-3d via Meshy REST API, as well as rigging + animating the models and integrating them into target game (the latter piece is where frontier models really stand out) I always have the agent create a local studio page so I can view and interact with the generated models! Tl;dr: Bowser T-pose image → 4 Blender reference images → Meshy image-to-3D → Blender cleanup (3M → 35K triangles) → Meshy auto-rigging → walk/run animations → interactive Three.js viewer

Rishi

69,345 views • 9 days ago

img2threejs 1.5.1 is out 🚀 Checkout: This release introduces a new approach to image → 3D → Three.js. Image → Tripo → img2threejs → reconstructed geometry → Three.js Instead of using the generated 3D model as the final asset, img2threejs uses it as a reference to rebuild the character into a representation that can be generated and rendered directly in Three.js. No Blender workflow, and no GLB required as the final output. The animation in the demo is still a bit rough because this release is focused on reconstruction, not animation. I’ll be improving that in the next release. Still experimental, but this is a big step toward where I want img2threejs to go.
1:02

img2threejs 1.5.1 is out 🚀 Checkout: This release introduces a new approach to image → 3D → Three.js. Image → Tripo → img2threejs → reconstructed geometry → Three.js Instead of using the generated 3D model as the final asset, img2threejs uses it as a reference to rebuild the character into a representation that can be generated and rendered directly in Three.js. No Blender workflow, and no GLB required as the final output. The animation in the demo is still a bit rough because this release is focused on reconstruction, not animation. I’ll be improving that in the next release. Still experimental, but this is a big step toward where I want img2threejs to go.

Nick

117,298 views • 22 days ago

Mattes and Utilities Demo: VFX artist Doug Hogan breaks down a ComfyUI workflow that generates production-ready utility passes directly from your footage. No manual rotoscoping. No guesswork. What's inside: → Prompt-based background removal (RMBG) for fast, clean shots → SAM3 text-prompt segmentation for moving subjects → SAM3 point-based editor for precise matte control → Face segmentation for beauty and skin work in comp → Stable depth and normals passes for relighting and CG integration → App Mode - a simplified UI that hides the node graph entirely Everything ties back to your source plate's native resolution and frame rate automatically via get/set nodes. This is the kind of workflow that used to take a team. Now it runs in ComfyUI. Click the link below for the workflow 👇
3:01

Mattes and Utilities Demo: VFX artist Doug Hogan breaks down a ComfyUI workflow that generates production-ready utility passes directly from your footage. No manual rotoscoping. No guesswork. What's inside: → Prompt-based background removal (RMBG) for fast, clean shots → SAM3 text-prompt segmentation for moving subjects → SAM3 point-based editor for precise matte control → Face segmentation for beauty and skin work in comp → Stable depth and normals passes for relighting and CG integration → App Mode - a simplified UI that hides the node graph entirely Everything ties back to your source plate's native resolution and frame rate automatically via get/set nodes. This is the kind of workflow that used to take a team. Now it runs in ComfyUI. Click the link below for the workflow 👇

ComfyUI

26,631 views • 3 months ago

New open-source 3D world-generation model. I'm rendering a couple of worlds in the video, so check it out. You'll find the GitHub and the Hugging Face links to the model below. This is a multi-modal world model that you can use for a bunch of things: • To generate new worlds • To reconstruct worlds • To simulate 3D interactive worlds from a prompt, images, or a video You can edit the 3D outputs in Unity and Unreal Engine (they export as meshes, 3DGS files, and point clouds). You can also generate 3D characters in the world and walk around. Pretty fun stuff!
2:41

New open-source 3D world-generation model. I'm rendering a couple of worlds in the video, so check it out. You'll find the GitHub and the Hugging Face links to the model below. This is a multi-modal world model that you can use for a bunch of things: • To generate new worlds • To reconstruct worlds • To simulate 3D interactive worlds from a prompt, images, or a video You can edit the 3D outputs in Unity and Unreal Engine (they export as meshes, 3DGS files, and point clouds). You can also generate 3D characters in the world and walk around. Pretty fun stuff!

Santiago

65,446 views • 5 months ago

What about a 2 day version? After Andrew Price posted about the two extremes (fully manual vs fully AI car modeling), I wanted to try a hybrid approach. Not sure if his examples were more film or game-focused, but I’m aiming for a game-ready asset. Using AssetHub for the main workflow hub. Because of the canvas and part splitting tools , iterating is smooth here. Generating high poly meshes with Tripo inside AssetHub. Retopoly with Hunyuan 3D Studio. Texturing with Trellis2 in ComfyUI. Did some manual modeling for the rims since i still dont trust AI generated rims being 100% circular😄 Assembly in Blender. End result: car exterior + interior 36K tris. Wheels total still 32K hehe. I’d bake those tire detail normals onto a simple cylinder next. In UEFN / Unreal Engine i used a clear coat material to give the car some nice reflections. Is it perfect? No. retopo and texturing are still the weak spots, but that gap is closing. Would I use this in a game? 100%! (after improving the tires). If you want to try AssetHub, you can sign up and use JR1627 to skip the waitlist.
1:39

What about a 2 day version? After Andrew Price posted about the two extremes (fully manual vs fully AI car modeling), I wanted to try a hybrid approach. Not sure if his examples were more film or game-focused, but I’m aiming for a game-ready asset. Using AssetHub for the main workflow hub. Because of the canvas and part splitting tools , iterating is smooth here. Generating high poly meshes with Tripo inside AssetHub. Retopoly with Hunyuan 3D Studio. Texturing with Trellis2 in ComfyUI. Did some manual modeling for the rims since i still dont trust AI generated rims being 100% circular😄 Assembly in Blender. End result: car exterior + interior 36K tris. Wheels total still 32K hehe. I’d bake those tire detail normals onto a simple cylinder next. In UEFN / Unreal Engine i used a clear coat material to give the car some nice reflections. Is it perfect? No. retopo and texturing are still the weak spots, but that gap is closing. Would I use this in a game? 100%! (after improving the tires). If you want to try AssetHub, you can sign up and use JR1627 to skip the waitlist.

Jerome | InsaneUnreal

26,608 views • 4 months ago

Now this ComfyUI experiment has some potential! #3 Tested the new Trellis 2 Image to 3D and i’m getting decent results. Separating the head and body helped a bit with some details. Also discovering useful helper workflows like removing backgrounds, upscaling and creating Normal maps. Still crazy how it just splits up an illustration like that! This example has 20K triangles, created from 1 front-view, which is not bad! I haven’t tested retopology yet, might have to check Tripo. Also want to test the multi-image setup. There is still some cleanup needed, but nothing to bad. Exploring several pipeline methods and so far the vertex color masking is helpful to keep everything in 1 material.
1:22

Now this ComfyUI experiment has some potential! #3 Tested the new Trellis 2 Image to 3D and i’m getting decent results. Separating the head and body helped a bit with some details. Also discovering useful helper workflows like removing backgrounds, upscaling and creating Normal maps. Still crazy how it just splits up an illustration like that! This example has 20K triangles, created from 1 front-view, which is not bad! I haven’t tested retopology yet, might have to check Tripo. Also want to test the multi-image setup. There is still some cleanup needed, but nothing to bad. Exploring several pipeline methods and so far the vertex color masking is helpful to keep everything in 1 material.

Jerome | InsaneUnreal

21,534 views • 8 months ago

Found a way to make nano-banana-pro insanely cost-effective on fal I built a workflow that generates 9 4K images for a total of $0.21. That’s just $0.023 per image. That is a ~92% cost reduction. The key is grid generation. I added a 'Crop Node' as a workflow utility specifically for this. It takes the grid output, splits it, and then we upscale everything using SeedVR
0:20

Found a way to make nano-banana-pro insanely cost-effective on fal I built a workflow that generates 9 4K images for a total of $0.21. That’s just $0.023 per image. That is a ~92% cost reduction. The key is grid generation. I added a 'Crop Node' as a workflow utility specifically for this. It takes the grid output, splits it, and then we upscale everything using SeedVR

ilker

148,418 views • 9 months ago