Video yükleniyor...

Video Yüklenemedi

Bu video yüklenirken bir sorun oluştu. Bu geçici bir ağ sorunundan kaynaklanıyor olabilir veya video kullanılamıyor olabilir.

Ana Sayfaya Dön

I'm thrilled to officially annouce🤩 📢 Automatic PBR : Image-to-material (I2M) 👉Textured Mesh in > Full PBR Mesh out Our latest foundation model for enriching existing 3D assets with the full array of PBR materials, whilst maintaining the same mesh geometry and UV unwrap. 🧵A thread : 1 of... show more

Elliott Round

1,191 subscribers

36,475 görüntüleme • 4 ay önce •via X (Twitter)

Eğitim Bilim & Teknoloji

Anya Rossi• Live Now

Private livecam show

0 Yorum

Yorum bulunmuyor

Orijinal gönderinin yorumları burada görünecek

Benzer Videolar

📢 Automatic PBR : Image-to-material (I2M) 👉Textured Mesh in > Full PBR Mesh out Our latest foundation model for enriching existing 3D assets with the full array of PBR materials, whilst maintaining the same mesh geometry and UV unwrap. #I2M #PBR #CV #Marso #texturing #gamedev #computervision M-XR

📢 Automatic PBR : Image-to-material (I2M) 👉Textured Mesh in > Full PBR Mesh out Our latest foundation model for enriching existing 3D assets with the full array of PBR materials, whilst maintaining the same mesh geometry and UV unwrap. #I2M #PBR #CV #Marso #texturing #gamedev #computervision M-XR

Elliott Round

13,423 görüntüleme • 4 ay önce

We start 2026 with a bang! Sparc3D v2.0 is now live on Scenario 🚨 Geometry-aware PBR texturing, shadow-free albedo, and sharper mesh reconstruction than v1.5... It's the kind of leap we love seeing, 2026 is going to be a big year for AI 3D. 👉

We start 2026 with a bang! Sparc3D v2.0 is now live on Scenario 🚨 Geometry-aware PBR texturing, shadow-free albedo, and sharper mesh reconstruction than v1.5... It's the kind of leap we love seeing, 2026 is going to be a big year for AI 3D. 👉

Emm | scenario.com

39,311 görüntüleme • 8 ay önce

Introducing the Marso Measure 3D Capture Programme 🎉 Calling all 3D scan artists! Get free access to Marso Measure, capture real-world assets, grow your portfolio, get noticed and win prizes. Starting January 2026. Prizes include: 🏆 Prize money of up to £3,000 for the best scanners 📸 Sony A7 Mark IV 💼 Annual Marso Measure License 🌍 Collaboration with M-XR partners 🎓 Certified Scan Artist status If you love 3D, PBR materials, and realism - apply now! Want to learn more? Check the comments for the full details and deck. #3D #VFX #3DScanning #MarsoMeasure #MXR #Photogrammetry #PBR #DigitalTwins

Introducing the Marso Measure 3D Capture Programme 🎉 Calling all 3D scan artists! Get free access to Marso Measure, capture real-world assets, grow your portfolio, get noticed and win prizes. Starting January 2026. Prizes include: 🏆 Prize money of up to £3,000 for the best scanners 📸 Sony A7 Mark IV 💼 Annual Marso Measure License 🌍 Collaboration with M-XR partners 🎓 Certified Scan Artist status If you love 3D, PBR materials, and realism - apply now! Want to learn more? Check the comments for the full details and deck. #3D #VFX #3DScanning #MarsoMeasure #MXR #Photogrammetry #PBR #DigitalTwins

M-XR

14,049 görüntüleme • 9 ay önce

📢 IntrinsiX: High-Quality PBR Generation using Image Priors 📢 From text input, we generate renderable PBR maps! Next to editable image generation, our predictions can be distilled into room-scale scenes using SDS for large-scale PBR texture generation. We first train separate LoRA modules for the intrinsic properties of albedo, rough/metal, normal. Then, we introduce cross-intrinsic attention using a rerendering loss with importance-weighted light sampling to enable coherent PBR generation. Our method outperforms text -> image -> PBR methods both in generalization and quality, since directly generating PBR maps does not suffer from the inherent ambiguity of intrinsic image decomposition. In addition, our design choice facilitates SDS-based PBR texture distillation. 🌍 🎥 Great work by Peter Kocsis, Lukas Höllein

📢 IntrinsiX: High-Quality PBR Generation using Image Priors 📢 From text input, we generate renderable PBR maps! Next to editable image generation, our predictions can be distilled into room-scale scenes using SDS for large-scale PBR texture generation. We first train separate LoRA modules for the intrinsic properties of albedo, rough/metal, normal. Then, we introduce cross-intrinsic attention using a rerendering loss with importance-weighted light sampling to enable coherent PBR generation. Our method outperforms text -> image -> PBR methods both in generalization and quality, since directly generating PBR maps does not suffer from the inherent ambiguity of intrinsic image decomposition. In addition, our design choice facilitates SDS-based PBR texture distillation. 🌍 🎥 Great work by Peter Kocsis, Lukas Höllein

Matthias Niessner

21,916 görüntüleme • 1 yıl önce

Trellis.2 and Pixal3D are now native in ComfyUI core No custom nodes. No CUDA extensions. No PyTorch downgrade. No non-commercial license traps. Plus a rebuilt 3D pipeline: Load/Preview/Save 3D nodes, mesh post-processing, and PBR texturing that bakes normals + AO for a full material set. Runs on consumer hardware. Free to use commercially. Click the link below to run the workflow and learn more ⬇️

Trellis.2 and Pixal3D are now native in ComfyUI core No custom nodes. No CUDA extensions. No PyTorch downgrade. No non-commercial license traps. Plus a rebuilt 3D pipeline: Load/Preview/Save 3D nodes, mesh post-processing, and PBR texturing that bakes normals + AO for a full material set. Runs on consumer hardware. Free to use commercially. Click the link below to run the workflow and learn more ⬇️

ComfyUI

32,808 görüntüleme • 4 gün önce

MICROSOFT DROPPED A 4B PARAMETER MODEL THAT TURNS ONE IMAGE INTO A 3D ASSET IN 3 SECONDS and it's open source TRELLIS.2 fully textured, physically accurate 3D models with PBR textures out of the box not a rough mesh..not a placeholder roughness, metallic, opacity the kind of detail that makes things look real under any lighting and it handles the weird stuff too..open surfaces, hollow interiors, geometry that breaks every other tool the model doesn't know the word "limitation" apparently demo is live on hugging face right now

MICROSOFT DROPPED A 4B PARAMETER MODEL THAT TURNS ONE IMAGE INTO A 3D ASSET IN 3 SECONDS and it's open source TRELLIS.2 fully textured, physically accurate 3D models with PBR textures out of the box not a rough mesh..not a placeholder roughness, metallic, opacity the kind of detail that makes things look real under any lighting and it handles the weird stuff too..open surfaces, hollow interiors, geometry that breaks every other tool the model doesn't know the word "limitation" apparently demo is live on hugging face right now

Vaishnavi

355,121 görüntüleme • 4 ay önce

Microsoft open-sourced a 4B model that turns any image into a production-ready 3D asset in 3 seconds. It’s called TRELLIS.2, a fully textured, physically accurate 3D models with PBR textures out of the box. → Full PBR (base color, roughness, metallic, opacity) → Handles hair, cloth, glass, non-manifold geometry → Exports .glb ready for Unity/Unreal/Blender → Runs locally, ships in 3 seconds It's not a demo or a research preview. The full training codebase is public. You can fine-tune it on your own asset library and get a model that generates in your studio's exact style. 100% Open Source

Microsoft open-sourced a 4B model that turns any image into a production-ready 3D asset in 3 seconds. It’s called TRELLIS.2, a fully textured, physically accurate 3D models with PBR textures out of the box. → Full PBR (base color, roughness, metallic, opacity) → Handles hair, cloth, glass, non-manifold geometry → Exports .glb ready for Unity/Unreal/Blender → Runs locally, ships in 3 seconds It's not a demo or a research preview. The full training codebase is public. You can fine-tune it on your own asset library and get a model that generates in your studio's exact style. 100% Open Source

Superman

282,803 görüntüleme • 1 ay önce

I built a playable 3D roguelike in Unreal Engine 5 from scratch in less than 48 hours. My accelerated workflow: ♦ Concepts — Nanobanana Pro ♦ 3D Models — Tripo 3D Smart Mesh (under 5-20k polygons, clean topology) ♦ Rigging — AccuRig to the UE5 skeleton ♦ Weight Painting — Quick Blender cleanup for rigid mesh elements ♦ PBR Tile Textures — Patina on ♦ Logic — UE5 Blueprint template + Claude Full workflow breakdown: #UE5 #GameDev #3DModeling #Blender3D #TechArt

I built a playable 3D roguelike in Unreal Engine 5 from scratch in less than 48 hours. My accelerated workflow: ♦ Concepts — Nanobanana Pro ♦ 3D Models — Tripo 3D Smart Mesh (under 5-20k polygons, clean topology) ♦ Rigging — AccuRig to the UE5 skeleton ♦ Weight Painting — Quick Blender cleanup for rigid mesh elements ♦ PBR Tile Textures — Patina on ♦ Logic — UE5 Blueprint template + Claude Full workflow breakdown: #UE5 #GameDev #3DModeling #Blender3D #TechArt

Stefan 3D AI

14,618 görüntüleme • 2 ay önce

🚀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:

🚀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:

Tencent Hy

58,225 görüntüleme • 1 yıl önce

#ByteDance dropped #Seed3D 2.0 and claims an 80% win rate against all major 3D AI tools. After dominating in the AI-video with #Seedance2, they are now entering the 3D generation. And the focus is no longer just textures — it’s about real usability of assets right after generation. I tested: 🔹Geometry (thin & complex structures) 🔹Part-level disassembly for simulations 🔹Unified PBR materials 🔹Compatibility to real game engines Watch the full breakdown — and let’s discuss: can this model actually change the industry?

#ByteDance dropped #Seed3D 2.0 and claims an 80% win rate against all major 3D AI tools. After dominating in the AI-video with #Seedance2, they are now entering the 3D generation. And the focus is no longer just textures — it’s about real usability of assets right after generation. I tested: 🔹Geometry (thin & complex structures) 🔹Part-level disassembly for simulations 🔹Unified PBR materials 🔹Compatibility to real game engines Watch the full breakdown — and let’s discuss: can this model actually change the industry?

Stefan 3D AI

18,827 görüntüleme • 4 ay önce

🚀Introducing Hunyuan3D-PolyGen, our newly upgraded and industry-first art-grade 3D generative model. It brings effortless intelligent retopology, making AI-generated models ready for professional art pipelines. ✅ Superior Mesh Topology: Our self-developed mesh autoregressive model ensures higher-quality mesh topology that meets stringent art standards. ✅ Complex Object Modeling: Leveraging our high-compression BPT representation, we can generate models with 10K+ faces, enabling more complex geometry, higher topology precision, and better detail. ✅ Flexible Output: Supports both tri and quad meshes, meeting diverse pipeline requirements. Hunyuan3D-PolyGen enables direct application of AI-generated 3D assets in game development and significantly boosts artist modeling efficiency. It's a robust foundation for the future of 3D content creation. 👉Try it now:

🚀Introducing Hunyuan3D-PolyGen, our newly upgraded and industry-first art-grade 3D generative model. It brings effortless intelligent retopology, making AI-generated models ready for professional art pipelines. ✅ Superior Mesh Topology: Our self-developed mesh autoregressive model ensures higher-quality mesh topology that meets stringent art standards. ✅ Complex Object Modeling: Leveraging our high-compression BPT representation, we can generate models with 10K+ faces, enabling more complex geometry, higher topology precision, and better detail. ✅ Flexible Output: Supports both tri and quad meshes, meeting diverse pipeline requirements. Hunyuan3D-PolyGen enables direct application of AI-generated 3D assets in game development and significantly boosts artist modeling efficiency. It's a robust foundation for the future of 3D content creation. 👉Try it now:

Tencent Hy

161,346 görüntüleme • 1 yıl önce

"#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.

"#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

2,177,191 görüntüleme • 1 yıl önce

An Object is Worth 64x64 Pixels: Generating 3D Object via Image Diffusion discuss: We introduce a new approach for generating realistic 3D models with UV maps through a representation termed "Object Images." This approach encapsulates surface geometry, appearance, and patch structures within a 64x64 pixel image, effectively converting complex 3D shapes into a more manageable 2D format. By doing so, we address the challenges of both geometric and semantic irregularity inherent in polygonal meshes. This method allows us to use image generation models, such as Diffusion Transformers, directly for 3D shape generation. Evaluated on the ABO dataset, our generated shapes with patch structures achieve point cloud FID comparable to recent 3D generative models, while naturally supporting PBR material generation.

An Object is Worth 64x64 Pixels: Generating 3D Object via Image Diffusion discuss: We introduce a new approach for generating realistic 3D models with UV maps through a representation termed "Object Images." This approach encapsulates surface geometry, appearance, and patch structures within a 64x64 pixel image, effectively converting complex 3D shapes into a more manageable 2D format. By doing so, we address the challenges of both geometric and semantic irregularity inherent in polygonal meshes. This method allows us to use image generation models, such as Diffusion Transformers, directly for 3D shape generation. Evaluated on the ABO dataset, our generated shapes with patch structures achieve point cloud FID comparable to recent 3D generative models, while naturally supporting PBR material generation.

AK

66,533 görüntüleme • 2 yıl önce

Flexible Isosurface Extraction for Gradient-Based Mesh Optimization paper page: This work considers gradient-based mesh optimization, where we iteratively optimize for a 3D surface mesh by representing it as the isosurface of a scalar field, an increasingly common paradigm in applications including photogrammetry, generative modeling, and inverse physics. Existing implementations adapt classic isosurface extraction algorithms like Marching Cubes or Dual Contouring; these techniques were designed to extract meshes from fixed, known fields, and in the optimization setting they lack the degrees of freedom to represent high-quality feature-preserving meshes, or suffer from numerical instabilities. We introduce FlexiCubes, an isosurface representation specifically designed for optimizing an unknown mesh with respect to geometric, visual, or even physical objectives. Our main insight is to introduce additional carefully-chosen parameters into the representation, which allow local flexible adjustments to the extracted mesh geometry and connectivity. These parameters are updated along with the underlying scalar field via automatic differentiation when optimizing for a downstream task. We base our extraction scheme on Dual Marching Cubes for improved topological properties, and present extensions to optionally generate tetrahedral and hierarchically-adaptive meshes. Extensive experiments validate FlexiCubes on both synthetic benchmarks and real-world applications, showing that it offers significant improvements in mesh quality and geometric fidelity.

Flexible Isosurface Extraction for Gradient-Based Mesh Optimization paper page: This work considers gradient-based mesh optimization, where we iteratively optimize for a 3D surface mesh by representing it as the isosurface of a scalar field, an increasingly common paradigm in applications including photogrammetry, generative modeling, and inverse physics. Existing implementations adapt classic isosurface extraction algorithms like Marching Cubes or Dual Contouring; these techniques were designed to extract meshes from fixed, known fields, and in the optimization setting they lack the degrees of freedom to represent high-quality feature-preserving meshes, or suffer from numerical instabilities. We introduce FlexiCubes, an isosurface representation specifically designed for optimizing an unknown mesh with respect to geometric, visual, or even physical objectives. Our main insight is to introduce additional carefully-chosen parameters into the representation, which allow local flexible adjustments to the extracted mesh geometry and connectivity. These parameters are updated along with the underlying scalar field via automatic differentiation when optimizing for a downstream task. We base our extraction scheme on Dual Marching Cubes for improved topological properties, and present extensions to optionally generate tetrahedral and hierarchically-adaptive meshes. Extensive experiments validate FlexiCubes on both synthetic benchmarks and real-world applications, showing that it offers significant improvements in mesh quality and geometric fidelity.

AK

38,452 görüntüleme • 3 yıl önce

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.

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

305,663 görüntüleme • 3 yıl önce

HOLY SHIT! Sparc 3D is INSANE! All they need to do now is enable PBR texture support, object separation and decimation and this in my opinion completely replaces photogrammetry. The quality of the reconstruction of these 3D objects from a single image is mind blowing to me. Yes, the model is insanely dense to allow all of this detail but the fact that AI can generate with this level of fidelity is CRAZY!!! You can try it for free on Huggingface

HOLY SHIT! Sparc 3D is INSANE! All they need to do now is enable PBR texture support, object separation and decimation and this in my opinion completely replaces photogrammetry. The quality of the reconstruction of these 3D objects from a single image is mind blowing to me. Yes, the model is insanely dense to allow all of this detail but the fact that AI can generate with this level of fidelity is CRAZY!!! You can try it for free on Huggingface

Travis Davids

18,230 görüntüleme • 1 yıl önce

Hi3D V3.0 is out today, and it is the most detailed and realistic 3D generator I have run. And its first model to build geometry at 2048³ voxels. The materials were the surprise. PBR output is well ahead of V2.1, and fine surface relief survives where the other frontier models smooth it flat. I compared it against the current top models across a wide spread of objects, textures on and textures off. It lands at number 2 in my ranking. For 3D printing I think it takes first outright. Also, they opened 48 hours of free access for the launch, which almost nobody does. Full breakdown:

Hi3D V3.0 is out today, and it is the most detailed and realistic 3D generator I have run. And its first model to build geometry at 2048³ voxels. The materials were the surprise. PBR output is well ahead of V2.1, and fine surface relief survives where the other frontier models smooth it flat. I compared it against the current top models across a wide spread of objects, textures on and textures off. It lands at number 2 in my ranking. For 3D printing I think it takes first outright. Also, they opened 48 hours of free access for the launch, which almost nobody does. Full breakdown:

Stefan 3D AI

12,020 görüntüleme • 16 gün önce

Nvidia announces GAvatar: Animatable 3D Gaussian Avatars with Implicit Mesh Learning paper page: Gaussian splatting has emerged as a powerful 3D representation that harnesses the advantages of both explicit (mesh) and implicit (NeRF) 3D representations. In this paper, we seek to leverage Gaussian splatting to generate realistic animatable avatars from textual descriptions, addressing the limitations (e.g., flexibility and efficiency) imposed by mesh or NeRF-based representations. However, a naive application of Gaussian splatting cannot generate high-quality animatable avatars and suffers from learning instability; it also cannot capture fine avatar geometries and often leads to degenerate body parts. To tackle these problems, we first propose a primitive-based 3D Gaussian representation where Gaussians are defined inside pose-driven primitives to facilitate animation. Second, to stabilize and amortize the learning of millions of Gaussians, we propose to use neural implicit fields to predict the Gaussian attributes (e.g., colors). Finally, to capture fine avatar geometries and extract detailed meshes, we propose a novel SDF-based implicit mesh learning approach for 3D Gaussians that regularizes the underlying geometries and extracts highly detailed textured meshes. Our proposed method, GAvatar, enables the large-scale generation of diverse animatable avatars using only text prompts. GAvatar significantly surpasses existing methods in terms of both appearance and geometry quality, and achieves extremely fast rendering (100 fps) at 1K resolution.

AK

141,051 görüntüleme • 2 yıl önce