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Stefan 3D AI

@Stefan_3D_AI8,308 subscribers

Stefan Vaskevich: 3D AI Explorer. Sharing my insights.

Shorts

I'm building my game with GPT-6, and color correction has become one of my favorite uses for it. When I generate assets separately, I can like each one on its own and still end up with a scene where the colors don't belong together. A castle looks fine in isolation, then turns greenish against the terrain. Its swords and shields disappear into the background. Getting those things to match used to mean a lot of repainting and trying different textures. Now I've built a workflow where GPT-6 takes screenshots in Unity, inspects the object's color textures, and helps bring them closer to the look I want. I can give it the terrain or tree textures as color references. It can prepare masks, send the relevant textures through Image Edit, then put the result back into the game for another look. Editors such as GPT Image 2 Edit or Nano Banana Edit can be part of that workflow. This is also why I keep pushing for properly prepared 3D models. If the parts are separated logically, you get much more control later. We could work on the swords and shields without changing the whole castle. The stone could stay dark while the equipment became easier to read. The latest pass on my game covered the overall post effects and the skeleton castles. We matched the castles to the surrounding terrain, adjusted the equipment colors, and added a subtle moving highlight. The first stronger color pass went too far. I asked for the accents to move about 30% closer to gray, and that got them where I wanted: visible, without pulling all the attention. I'm so happy with the difference. The video shows the before and after across the map and on the castles. Being able to give feedback on the actual scene and keep refining it this way feels great.

I'm building my game with GPT-6, and color correction has become one of my favorite uses for it. When I generate assets separately, I can like each one on its own and still end up with a scene where the colors don't belong together. A castle looks fine in isolation, then turns greenish against the terrain. Its swords and shields disappear into the background. Getting those things to match used to mean a lot of repainting and trying different textures. Now I've built a workflow where GPT-6 takes screenshots in Unity, inspects the object's color textures, and helps bring them closer to the look I want. I can give it the terrain or tree textures as color references. It can prepare masks, send the relevant textures through Image Edit, then put the result back into the game for another look. Editors such as GPT Image 2 Edit or Nano Banana Edit can be part of that workflow. This is also why I keep pushing for properly prepared 3D models. If the parts are separated logically, you get much more control later. We could work on the swords and shields without changing the whole castle. The stone could stay dark while the equipment became easier to read. The latest pass on my game covered the overall post effects and the skeleton castles. We matched the castles to the surrounding terrain, adjusted the equipment colors, and added a subtle moving highlight. The first stronger color pass went too far. I asked for the accents to move about 30% closer to gray, and that got them where I wanted: visible, without pulling all the attention. I'm so happy with the difference. The video shows the before and after across the map and on the castles. Being able to give feedback on the actual scene and keep refining it this way feels great.

16,093 görüntüleme

Another big drop from Hunyuan3D by Tencent. Hunyuan3D-Buffalo 1.0 is not just another text-to-3D model. The more interesting part is its approach to 3D understanding and editing. It can understand separate parts of a complete asset, extract them, remove or replace them with prompts, and edit only the selected region while keeping the rest of the geometry unchanged. That feels like a much more useful direction for 3D AI in general: not just generating a mesh once, but actually understanding its structure and letting you continue working with it. The model combines Qwen-VL, TRELLIS and Hunyuan3D, and was trained on an impressive dataset of 87 million 3D samples. Source:

Another big drop from Hunyuan3D by Tencent. Hunyuan3D-Buffalo 1.0 is not just another text-to-3D model. The more interesting part is its approach to 3D understanding and editing. It can understand separate parts of a complete asset, extract them, remove or replace them with prompts, and edit only the selected region while keeping the rest of the geometry unchanged. That feels like a much more useful direction for 3D AI in general: not just generating a mesh once, but actually understanding its structure and letting you continue working with it. The model combines Qwen-VL, TRELLIS and Hunyuan3D, and was trained on an impressive dataset of 87 million 3D samples. Source:

36,034 görüntüleme

Okay this is kinda wild 👀 NVIDIA is basically handing out FREE API keys to 100+ top AI models - GLM 5.2, DeepSeek V4, Kimi K2.6, MiniMax M3, their own Nemotron and a ton more. it's called NVIDIA NIM and I had no idea it existed. it's rate-limited, so not something you'd run in production - but for personal use it's honestly great. you can poke a dozen frontier models, and some you can even grab and self-host. dropped a key into OpenCode and it just worked. the hyped ones like GLM 5.2 are jammed with queue right now, but the lighter models fly. all of it free.

Okay this is kinda wild 👀 NVIDIA is basically handing out FREE API keys to 100+ top AI models - GLM 5.2, DeepSeek V4, Kimi K2.6, MiniMax M3, their own Nemotron and a ton more. it's called NVIDIA NIM and I had no idea it existed. it's rate-limited, so not something you'd run in production - but for personal use it's honestly great. you can poke a dozen frontier models, and some you can even grab and self-host. dropped a key into OpenCode and it just worked. the hyped ones like GLM 5.2 are jammed with queue right now, but the lighter models fly. all of it free.

26,272 görüntüleme

Videos

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Day 11. Building the game I've dreamed of for 15 years with AI. I thought humanoid animations would be the easy part. Then I spent half the night trying to get a skeleton to swing its arm. I wanted that awkward, broken skeleton movement. GPT-6 Astra couldn't get the attack right in Blender on its own, even though other parts were working. Time to bring in some of my own animation knowledge. Three things I tried: 1. Recorded myself doing the arm swing, turned the video into mocap, and gave that animation to Codex. I asked it to fix just the arm, keeping the parts that already worked. 2. Put my generated Seedance video references through mocap too. They converted surprisingly well. The free option I tried was Unreal Engine 5.8; Quick Magic was the paid one. Both gave me motion I could use. 3. Let the agent try Cascadeur through its MCP server. It set up AutoPosing and experimented with AutoPhysics. This part is still inconclusive for me. The MCP integration felt too limited to call it a reliable part of the workflow yet. GPT-6 took those mocap clips and assembled the final animations. About six or seven rounds, roughly half an hour each. Today I got the skeletons into the game, and I'm so happy with how they turned out. I'm already working on the archers. For difficult movements, I'll be using mocap references much more often now. GPT can take just an arm movement from a clip and adjust the rest itself. Being able to combine those two approaches is what finally got this working for me.

Stefan 3D AI

145,425 görüntüleme • 5 gün önce

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I got this custom scorpion rigged, animated and running in my game in one full day with AI. GPT-6 built the entire rig. My part was preparing the model and motion references, then giving feedback. After trying a few approaches, I found a workflow that worked: 1. Give it a model that makes sense. I generated the model with Tripo P2.0, then cleaned it up myself. Low-poly, optimized, split into logical parts. I think that preparation mattered a lot. GPT-6 works on the mesh through code. A manageable vertex count and sensible parts give it a clearer starting point for bone placement and skinning. 2. Show it the movement you want. I took a reference image of the character and used Seedance to generate side-view animation videos. I decided which animations the game needed and selected the takes with the movements I liked. Then I gave GPT-6 the model, the videos and that animation list. There was no existing rig to start from. 3. Compare the animation frame by frame. This was the idea I wanted to test: ask GPT-6 to compare its Blender animation against the reference video frame by frame, from the same view. That gave it concrete poses and timing to work toward. I could point to where the feet should be planted or when the tail should strike. 4. Expect to give feedback. A generated run can look like hopping. An attack can start with a jump you never wanted. Choosing the references matters, and a side view still leaves depth and hidden legs to figure out. It took several iterations. I explained what looked wrong and how I wanted it to move, and GPT-6 kept refining the animation. Once the walk worked, I also had it create left and right strafe animations procedurally from that walk. Those didn't need their own reference videos. By the end of the day, the creature was in Unity. I've recorded the full workflow, including the Blender setup and feedback process:

Stefan 3D AI

12,141 görüntüleme • 4 gün önce