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Maxon + Tencent Cloud bring HY 3D to #Cinema4D ✨ Introducing AI-assisted workflows that let artists generate base 3D & UV models from text prompts or image references. Fully customizable in #C4D & compatible with #ZBrush, #Redshift, & #RedGiant. 🔗

Maxon

78,596 subscribers

67,124 views • 5 months ago •via X (Twitter)

Arts Education Science & Technology #Cinema4D #C4D #ZBrush #Redshift #RedGiant

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3D AI is leveling up! Rodin 3D AI can create stunning, high-quality 3D models from just text or image inputs. And with its latest update, it can even generate 8K HDRI textures to bring your models to life. Check out the link in the comments!

3D AI is leveling up! Rodin 3D AI can create stunning, high-quality 3D models from just text or image inputs. And with its latest update, it can even generate 8K HDRI textures to bring your models to life. Check out the link in the comments!

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🚀Exciting News! Tencent Hunyuan 3D AI Creation Engine v2.5 is here, revolutionizing 3D generation with ultra-high-definition modeling! 1⃣Massive Leap in Precision: Upgraded geometric model with 10B parameters (up from 1B) and 10x more mesh faces, delivering 1024-resolution geometric details—smoother surfaces, sharper edges, and richer visuals! 2⃣High-quality Textures & PBR: Supports high-quality textures and fine-grained bump mapping for lifelike surfaces. Industry-first multi-view PBR generation for realistic lighting and reflections. 3⃣Animation Boost: Optimized skeletal rigging with automatic bone binding and skin weight assignment, streamlining 3D animation workflows. 4⃣Flexible Workflows: New pipeline templates for text-to-3D and image-to-3D mesh reduction, plus multi-view 3D generation, empowering game development, animation, and more with customizable 3D assets. 5⃣Accessible Creation: Generate high-quality 3D models from text or images, slashing complexity. Free daily quota doubled to 20 for the AI Engine, with the API now live on Tencent Cloud! ☀️From games to AR/VR, Hunyuan 3D is redefining 3D AIGC, making creation faster, smarter, and more inclusive. Try it now! Tencent Hunyuan 3D AI Creation Engine: Tencent Hunyuan 3D Open Source:

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Tencent Hy

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Tencent Hy

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Phoenix AI

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311,073 views • 1 year ago

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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

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48,259 views • 2 years ago

This is peak... Google just unveiled Genie 3 This AI generates photorealistic & 3D worlds from a text prompt and image... that you can explore in real-time Clearing a path towards AGI 10 wild examples + how to try below 1. Control a shiny marble

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44,926 views • 6 months ago

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Meet Tencent HY 3D Studio 1.2 👋 With this major upgrade to our 3D creation pipeline, you can generate assets with sculpt-level detail and fine-grained interactive control. Starting today, the studio is officially open for Public Beta — no application required. Tencent HY 3D Studio 1.2 introduces a suite of features built for creative precision, including: 🧩 PartGen 1.5: High-Precision Component Partitioning — 1536³ Resolution: Boosted from 1024³ for crystal-clear model splitting and ultra-fine detail retention. — Fine-Grained Interaction: Introducing intuitive, brush-based control for precise, manual component editing. — Shape Adherence: Drastically improved geometry integrity for even the most intricate objects. 🎨 Tencent HY 3D 3.1: Sculpt-Level Detail — Enhanced Geometry: Fine-grained details that adapt perfectly to stylized inputs.. — Texture Fidelity: A massive leap in color accuracy and texture fidelity, reflecting your original input with stunning realism. — 8-View Control: Expanded from 4 to 8 input views for ultimate reconstruction accuracy. ✨We’re so excited to see what you create! Try it now:

Tencent Hy

83,314 views • 7 months ago

We’re open-sourcing HY-World 2.0, a multimodal world model that generates, reconstructs, and simulates interactive *3D worlds* from text, images, and videos. Outputs can be integrated into game engines and embodied simulation pipelines. Key highlights: 🔹 One-click world generation Turn text or image into interactive 3D worlds automatically. 🔹 Pipeline-ready 3D outputs Editable 3D worlds for Unity and Unreal Engine, with standard 3D exports including mesh, 3DGS, and point clouds. 🔹 Unified world model system One model family for world generation and reconstruction across synthetic and real-world scenes. 🔹 Interactive character mode Explore generated 3D worlds in real time with physics-aware movement and collision support. ✨ Apply for access: 🔗 GitHub: 🤗 Hugging Face: 📄 Technical Report:

We’re open-sourcing HY-World 2.0, a multimodal world model that generates, reconstructs, and simulates interactive 3D worlds from text, images, and videos. Outputs can be integrated into game engines and embodied simulation pipelines. Key highlights: 🔹 One-click world generation Turn text or image into interactive 3D worlds automatically. 🔹 Pipeline-ready 3D outputs Editable 3D worlds for Unity and Unreal Engine, with standard 3D exports including mesh, 3DGS, and point clouds. 🔹 Unified world model system One model family for world generation and reconstruction across synthetic and real-world scenes. 🔹 Interactive character mode Explore generated 3D worlds in real time with physics-aware movement and collision support. ✨ Apply for access: 🔗 GitHub: 🤗 Hugging Face: 📄 Technical Report:

Tencent Hy

371,581 views • 4 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?

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

LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models paper page: github: Recent advancements in text-to-image generation with diffusion models have yielded remarkable results synthesizing highly realistic and diverse images. However, these models still encounter difficulties when generating images from prompts that demand spatial or common sense reasoning. We propose to equip diffusion models with enhanced reasoning capabilities by using off-the-shelf pretrained large language models (LLMs) in a novel two-stage generation process. First, we adapt an LLM to be a text-guided layout generator through in-context learning. When provided with an image prompt, an LLM outputs a scene layout in the form of bounding boxes along with corresponding individual descriptions. Second, we steer a diffusion model with a novel controller to generate images conditioned on the layout. Both stages utilize frozen pretrained models without any LLM or diffusion model parameter optimization. We validate the superiority of our design by demonstrating its ability to outperform the base diffusion model in accurately generating images according to prompts that necessitate both language and spatial reasoning. Additionally, our method naturally allows dialog-based scene specification and is able to handle prompts in a language that is not well-supported by the underlying diffusion model.

LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models paper page: github: Recent advancements in text-to-image generation with diffusion models have yielded remarkable results synthesizing highly realistic and diverse images. However, these models still encounter difficulties when generating images from prompts that demand spatial or common sense reasoning. We propose to equip diffusion models with enhanced reasoning capabilities by using off-the-shelf pretrained large language models (LLMs) in a novel two-stage generation process. First, we adapt an LLM to be a text-guided layout generator through in-context learning. When provided with an image prompt, an LLM outputs a scene layout in the form of bounding boxes along with corresponding individual descriptions. Second, we steer a diffusion model with a novel controller to generate images conditioned on the layout. Both stages utilize frozen pretrained models without any LLM or diffusion model parameter optimization. We validate the superiority of our design by demonstrating its ability to outperform the base diffusion model in accurately generating images according to prompts that necessitate both language and spatial reasoning. Additionally, our method naturally allows dialog-based scene specification and is able to handle prompts in a language that is not well-supported by the underlying diffusion model.

AK

83,657 views • 3 years ago

I've been a proponent of the idea of combining 3d and generative ai for a long time - as a way to get the best of both worlds: The precision and control of 3d - the detail, realism, lighting and speed of generative models. Here's another example of that idea, realized through a procedural system - entirely vibe-coded with Fable. Inspired by a photograph I took of an old tower in a town in southern France. In the first part of the video I'm using Krea AI's realtime model - later examples show how Nano Banana 2 can be used to take 3d stills and transform them to pretty much anything. What do you think of workflows like this compared to purely generating video or images from text, image or video input?

I've been a proponent of the idea of combining 3d and generative ai for a long time - as a way to get the best of both worlds: The precision and control of 3d - the detail, realism, lighting and speed of generative models. Here's another example of that idea, realized through a procedural system - entirely vibe-coded with Fable. Inspired by a photograph I took of an old tower in a town in southern France. In the first part of the video I'm using Krea AI's realtime model - later examples show how Nano Banana 2 can be used to take 3d stills and transform them to pretty much anything. What do you think of workflows like this compared to purely generating video or images from text, image or video input?

Martin Nebelong

20,265 views • 14 days ago

Bring your dream to the screen. The CapCut CRE[AI]TE AI Festival is here to find the next generation of creators. Submit your work for a chance to win a $70,000 grand prize, plus gain a global audience for your ideas. To enter the festival, create an AI film, episodic series, social-first creative experiment, or spec commercial in CapCut Video Studio, an all-in-one video workspace that helps make ideas come alive. With Video Studio, you can: -> Brainstorm on an infinite canvas that combines text-based prompts with imagery, video, and audio references -> Turn scripts into shot lists and storyboards -> Generate clips -> Create fully produced videos Submit your work by August 10 to

Bring your dream to the screen. The CapCut CRE[AI]TE AI Festival is here to find the next generation of creators. Submit your work for a chance to win a $70,000 grand prize, plus gain a global audience for your ideas. To enter the festival, create an AI film, episodic series, social-first creative experiment, or spec commercial in CapCut Video Studio, an all-in-one video workspace that helps make ideas come alive. With Video Studio, you can: -> Brainstorm on an infinite canvas that combines text-based prompts with imagery, video, and audio references -> Turn scripts into shot lists and storyboards -> Generate clips -> Create fully produced videos Submit your work by August 10 to

CapCut

347,156 views • 1 month ago

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.

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

82,367 views • 4 months ago