正在加载视频...

视频加载失败

加载此视频时出现问题。这可能是由于临时网络问题，或视频可能不可用。

converts single 2D images into interactable 3D models; with MonoArt. Interior designers will definitely appreciate this one. - Snap one photo of any furniture - Get a working 3D model in 20 seconds - jointly predicts geometry, part segments, and motion; - built on TRELLIS. Drawers actually slide, doors... show more

Wildminder

9,294 subscribers

25,185 次观看 • 6 个月前 •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

0 条评论

暂无评论

原始帖子的评论将显示在这里

相关视频

⚡️ Introducing Bolt3D ⚡️ Bolt3D generates interactive 3D scenes in less than 7 seconds on a single GPU from one or more images. It features a latent diffusion model that *directly* generates 3D Gaussians of seen and unseen regions, without any test time optimization. 🧵👇 (1/9)

⚡️ Introducing Bolt3D ⚡️ Bolt3D generates interactive 3D scenes in less than 7 seconds on a single GPU from one or more images. It features a latent diffusion model that directly generates 3D Gaussians of seen and unseen regions, without any test time optimization. 🧵👇 (1/9)

Stan Szymanowicz

126,142 次观看 • 1 年前

If you own a 3D printer, you need this tool on your radar right now. SNAP3D turns a single 2D image into fully assemblable, multi-part 3D prints with built-in peg-and-socket joints! No glue. No screws. Just print, snap, and build. it cuts the model into semantic parts, optimizes the connectors, and runs physics tests to guarantee stability. Feed it a photo, get real-world snap-fit parts ready for your print bed

If you own a 3D printer, you need this tool on your radar right now. SNAP3D turns a single 2D image into fully assemblable, multi-part 3D prints with built-in peg-and-socket joints! No glue. No screws. Just print, snap, and build. it cuts the model into semantic parts, optimizes the connectors, and runs physics tests to guarantee stability. Feed it a photo, get real-world snap-fit parts ready for your print bed

Wildminder

44,599 次观看 • 27 天前

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

355,784 次观看 • 5 个月前

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

285,151 次观看 • 2 个月前

2D is what the camera sees.👀 3D is what the world is. 🌍 Today, we’re proudly open-sourcing QuerySplat — an open-source feed-forward 3D model that turns a few unposed images into a navigable 3D scene in seconds (run on 4090🪶). Your input: a few unposed images Our output: a sharp, navigable 3D scene — generated in seconds! 🚀 🧠 organize scenes with 3D queries, not pixel-bound Gaussians 📷 no camera poses or manual calibration required ⚡ reconstruct new scenes in a single forward pass ✨ preserve sharp geometry and high-frequency appearance 🏆 achieve state-of-the-art results on DL3DV-Evaluation 🔓 code and model weights are now open-source From pixels to scenes, we are making 3D generation scalable. Try our free iOS app — it’s a lot of fun! 😆 🔗 Try it on the Web: 🔗 Paper: 🔗 Code & weights: 🔗 Project page: 🔗 #AIGC #3DGS #AI #Computervision #3D #artist #3dartist #3d

2D is what the camera sees.👀 3D is what the world is. 🌍 Today, we’re proudly open-sourcing QuerySplat — an open-source feed-forward 3D model that turns a few unposed images into a navigable 3D scene in seconds (run on 4090🪶). Your input: a few unposed images Our output: a sharp, navigable 3D scene — generated in seconds! 🚀 🧠 organize scenes with 3D queries, not pixel-bound Gaussians 📷 no camera poses or manual calibration required ⚡ reconstruct new scenes in a single forward pass ✨ preserve sharp geometry and high-frequency appearance 🏆 achieve state-of-the-art results on DL3DV-Evaluation 🔓 code and model weights are now open-source From pixels to scenes, we are making 3D generation scalable. Try our free iOS app — it’s a lot of fun! 😆 🔗 Try it on the Web: 🔗 Paper: 🔗 Code & weights: 🔗 Project page: 🔗 #AIGC #3DGS #AI #Computervision #3D #artist #3dartist #3d

InSpatio｜影溯

29,806 次观看 • 2 个月前

China open-sourced a model that reconstructs any scene in 3D from a regular video, in real-time. one camera. no LiDAR. 10,000+ frames without falling apart. just walk around with your camera and watch the entire world get rebuilt in 3D at 20 fps. → runs at ~20 FPS on a single GPU → Stable over 10,000+ frames → Beats optimization-based methods on benchmarks → Works on drone footage, driving videos, indoor walkthroughs 100% open source.

China open-sourced a model that reconstructs any scene in 3D from a regular video, in real-time. one camera. no LiDAR. 10,000+ frames without falling apart. just walk around with your camera and watch the entire world get rebuilt in 3D at 20 fps. → runs at ~20 FPS on a single GPU → Stable over 10,000+ frames → Beats optimization-based methods on benchmarks → Works on drone footage, driving videos, indoor walkthroughs 100% open source.

Yasir Ai

254,803 次观看 • 2 个月前

China open-sourced a model that reconstructs any scene in 3D from a regular video, in real-time. one camera. no LiDAR. 10,000+ frames without falling apart. just walk around with your camera and watch the entire world get rebuilt in 3D at 20 fps. → runs at ~20 FPS on a single GPU → Stable over 10,000+ frames → Beats optimization-based methods on benchmarks → Works on drone footage, driving videos, indoor walkthroughs 100% open source.

China open-sourced a model that reconstructs any scene in 3D from a regular video, in real-time. one camera. no LiDAR. 10,000+ frames without falling apart. just walk around with your camera and watch the entire world get rebuilt in 3D at 20 fps. → runs at ~20 FPS on a single GPU → Stable over 10,000+ frames → Beats optimization-based methods on benchmarks → Works on drone footage, driving videos, indoor walkthroughs 100% open source.

Superman

1,459,380 次观看 • 2 个月前

How to split a 3D model into printable parts in one click? Here’s the full workflow with Meshy Auto Split: - Generate a draft model with Image-to-3D - Hit Auto Split. In 40 seconds, you get parts that follow the model’s natural structure - Download and open your slicer Every part comes out clean, watertight, and ready to print. Connectors? We hear you. Coming soon 👀

How to split a 3D model into printable parts in one click? Here’s the full workflow with Meshy Auto Split: - Generate a draft model with Image-to-3D - Hit Auto Split. In 40 seconds, you get parts that follow the model’s natural structure - Download and open your slicer Every part comes out clean, watertight, and ready to print. Connectors? We hear you. Coming soon 👀

MeshyAI

71,403 次观看 • 3 个月前

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

305,746 次观看 • 3 年前

I plugged ChatGPT Luna 5.6 and Claude Opus 5 into Blender through MCP, and one of them is one-shotting entire 3D scenes now. Top is Luna 5.6 in fast mode modeling ship cannons with a firing animation. Watching the scene grow mesh by mesh in real time is a separate kind of dopamine. Bottom is Opus 5 building a full carriage scene with terrain, rocks and lighting. This one is simply built for 3D -- the whole thing from a single prompt with zero retries and zero corrections. The entire bridge is one open-source repo, no Python and no modeling skills required: > The repo is with 16.9k stars under MIT license. > It works with any model -- Claude, GPT, Cursor, any MCP client. > The model sees your scene through a live socket and edits it in real time. A year ago this workflow didn't exist, and now the gap between an idea and a rendered 3D scene is one prompt. Save this one -- you will come back for the repo ↓

I plugged ChatGPT Luna 5.6 and Claude Opus 5 into Blender through MCP, and one of them is one-shotting entire 3D scenes now. Top is Luna 5.6 in fast mode modeling ship cannons with a firing animation. Watching the scene grow mesh by mesh in real time is a separate kind of dopamine. Bottom is Opus 5 building a full carriage scene with terrain, rocks and lighting. This one is simply built for 3D -- the whole thing from a single prompt with zero retries and zero corrections. The entire bridge is one open-source repo, no Python and no modeling skills required: > The repo is with 16.9k stars under MIT license. > It works with any model -- Claude, GPT, Cursor, any MCP client. > The model sees your scene through a live socket and edits it in real time. A year ago this workflow didn't exist, and now the gap between an idea and a rendered 3D scene is one prompt. Save this one -- you will come back for the repo ↓

slash1s

150,102 次观看 • 2 个月前

This first-person shooter was built from a single prompt, using 404-generated 3D assets. On the latest Novelty Search, 404 explains how SN17 is moving from Titan, its open image-to-3D model built with miners, toward 3D assets generated as code that AI agents can edit and use directly in games. That work is part of a broader direction for 404: building world models where AI agents can learn and act. Hosted by const Full episode in the first comment

This first-person shooter was built from a single prompt, using 404-generated 3D assets. On the latest Novelty Search, 404 explains how SN17 is moving from Titan, its open image-to-3D model built with miners, toward 3D assets generated as code that AI agents can edit and use directly in games. That work is part of a broader direction for 404: building world models where AI agents can learn and act. Hosted by const Full episode in the first comment

Openτensor Foundaτion

19,046 次观看 • 1 个月前

Today, we are open-sourcing Hunyuan World 1.1 (WorldMirror), a universal feed-forward 3D reconstruction model. 🚀🚀🚀 While our previously released Hunyuan World 1.0 (open-sourced, lite version deployable on consumer GPUs) focused on generating 3D worlds from text or single-view images, Hunyuan World 1.1 significantly expands the input scope by unlocking video-to-3D and multi-view-to-3D world creation. Highlights: 🔹Any Input, Maximized Flexibility and Fidelity: Flexibly integrates diverse geometric priors (camera poses, intrinsics, depth maps) to resolve structural ambiguities and ensure geometrically consistent 3D outputs. 🔹Any Output, SOTA Results：This elegant architecture simultaneously generates multiple 3D representations: dense point clouds, multi-view depth maps, camera parameters, surface normals, and 3D Gaussian Splattings. 🔹Single-GPU & Fast Inference: As an all-in-one, feed-forward model, Hunyuan World 1.1 runs on a single GPU and delivers all 3D attributes in a single forward pass, within seconds. 🌐Project Page: 🔗Github： 🤗Hugging Face： ✨Demo: 📄Technical Report:

Today, we are open-sourcing Hunyuan World 1.1 (WorldMirror), a universal feed-forward 3D reconstruction model. 🚀🚀🚀 While our previously released Hunyuan World 1.0 (open-sourced, lite version deployable on consumer GPUs) focused on generating 3D worlds from text or single-view images, Hunyuan World 1.1 significantly expands the input scope by unlocking video-to-3D and multi-view-to-3D world creation. Highlights: 🔹Any Input, Maximized Flexibility and Fidelity: Flexibly integrates diverse geometric priors (camera poses, intrinsics, depth maps) to resolve structural ambiguities and ensure geometrically consistent 3D outputs. 🔹Any Output, SOTA Results：This elegant architecture simultaneously generates multiple 3D representations: dense point clouds, multi-view depth maps, camera parameters, surface normals, and 3D Gaussian Splattings. 🔹Single-GPU & Fast Inference: As an all-in-one, feed-forward model, Hunyuan World 1.1 runs on a single GPU and delivers all 3D attributes in a single forward pass, within seconds. 🌐Project Page: 🔗Github： 🤗Hugging Face： ✨Demo: 📄Technical Report:

Tencent Hy

168,848 次观看 • 11 个月前

I've been working on web projects with Three.js more and more lately — 2D, 3D, XR — and the one thing I've noticed is the lack of tools compared to game engines. That's why I decided to start building on the side a library of 3D assets that are actually little programs, AI-native, and fully customizable. I called this @polyfork_ , and it's already being used by dozens of people that have an AI centric workflow, since the website can be fully understood and used by agents. Assets are being built each day, at a rate of 100+ a week, and I'm planning to make it the biggest programmatic-3D library on the internet. Almost half of the models are free, and I opened slots for "founding" members, who can make a one-time payment for lifetime access, with access to all current models and kits, plus everything built from now on. Get one before they run out.

I've been working on web projects with Three.js more and more lately — 2D, 3D, XR — and the one thing I've noticed is the lack of tools compared to game engines. That's why I decided to start building on the side a library of 3D assets that are actually little programs, AI-native, and fully customizable. I called this @polyfork_ , and it's already being used by dozens of people that have an AI centric workflow, since the website can be fully understood and used by agents. Assets are being built each day, at a rate of 100+ a week, and I'm planning to make it the biggest programmatic-3D library on the internet. Almost half of the models are free, and I opened slots for "founding" members, who can make a one-time payment for lifetime access, with access to all current models and kits, plus everything built from now on. Get one before they run out.

Lucas Martinic

48,350 次观看 • 2 个月前

This tool can learn any 3d spaces with just an image. It’s called OneCanvas, a NeurIPS 2026 Spotlight paper with public code and model. It merges video frames of a room into one panorama, so an ordinary image AI can answer questions about 3D space. → New state of the art on spatial reasoning benchmarks → Answers from any viewpoint, including the AI’s own position in the room → Needs depth data and camera positions, not just plain video 100% free & open-source.

This tool can learn any 3d spaces with just an image. It’s called OneCanvas, a NeurIPS 2026 Spotlight paper with public code and model. It merges video frames of a room into one panorama, so an ordinary image AI can answer questions about 3D space. → New state of the art on spatial reasoning benchmarks → Answers from any viewpoint, including the AI’s own position in the room → Needs depth data and camera positions, not just plain video 100% free & open-source.

Simplifying AI

18,608 次观看 • 7 天前

We are introducing Hunyuan3D-Part: an open-source part-level 3D shape generation model that outperforms all existing open and close-source models. Highlights: 🔹P3-SAM: The industry's first native 3D part segmentation model. 🔹X-Part: A part generation model that achieves state-of-the-art results in controllability and shape quality. Key-features: 1️⃣Eliminates the use of 2D SAM during training, relying solely on a large-scale dataset with 3.7 million shapes and clean part annotations. 2️⃣Introduces a new automated segmentation pipeline in 3D without user intervention. 3️⃣Implements a diffusion-based part decomposition pipeline utilizing both geometry and semantic clues. Code: Weights: Tech reports: 🔸P3-SAM： → Paper: → Project page: 🔸X-Part： → Paper: → Project page: Try it now： → (Light version) Hugging Face demo: → (Full version) Hunyuan3D Studio:

We are introducing Hunyuan3D-Part: an open-source part-level 3D shape generation model that outperforms all existing open and close-source models. Highlights: 🔹P3-SAM: The industry's first native 3D part segmentation model. 🔹X-Part: A part generation model that achieves state-of-the-art results in controllability and shape quality. Key-features: 1️⃣Eliminates the use of 2D SAM during training, relying solely on a large-scale dataset with 3.7 million shapes and clean part annotations. 2️⃣Introduces a new automated segmentation pipeline in 3D without user intervention. 3️⃣Implements a diffusion-based part decomposition pipeline utilizing both geometry and semantic clues. Code: Weights: Tech reports: 🔸P3-SAM： → Paper: → Project page: 🔸X-Part： → Paper: → Project page: Try it now： → (Light version) Hugging Face demo: → (Full version) Hunyuan3D Studio:

Tencent Hy

72,640 次观看 • 1 年前

I open-sourced Dioramas, a framework for cinematic, interactive 3D websites. It's 100% free. No paid tier, no signup. It comes with 20 full example sites, each built around one interaction you can touch: a museum you explore by lantern, a mech you power up, a geode you crack open. It's powered by fal: every 3D model was generated with Meshy 7.1 from Nano Banana 2 images. Every prompt, model and line of code is in the repo. Repo:

I open-sourced Dioramas, a framework for cinematic, interactive 3D websites. It's 100% free. No paid tier, no signup. It comes with 20 full example sites, each built around one interaction you can touch: a museum you explore by lantern, a mech you power up, a geode you crack open. It's powered by fal: every 3D model was generated with Meshy 7.1 from Nano Banana 2 images. Every prompt, model and line of code is in the repo. Repo:

Blendi

28,175 次观看 • 12 天前

THIS GUY VIBE CODED A TRUE 3D PROPERTY TOUR TOOL WITH CLAUDE CODE not the fake 360 photo stitching that matterport does, actual 3D right now if you're a real estate agent and want a property tour you need: > a matterport camera ($3000+) > a photographer who knows how to use it > matterport's monthly subscription > and you still get a glorified panorama, not real 3D this guy built a tool that takes your existing captures (camera files, drone shots, 360 photos, EVEN phone captures) and turns them into a real interactive 3D tour in one workflow no specialized hardware, no photographer, no switching between 5 different tools real estate agents, property managers, airbnb hosts, architects, interior designers all of them are paying hundreds per property for something this tool does from captures they already have the painful part was never understanding the end result. everyone knows what a 3D tour looks like. the pain is going from a pile of messy capture files to something usable without it turning into a technical project this solves that with one workflow you dump your files in and get a 3D tour out insane what you can do with vibe coding

THIS GUY VIBE CODED A TRUE 3D PROPERTY TOUR TOOL WITH CLAUDE CODE not the fake 360 photo stitching that matterport does, actual 3D right now if you're a real estate agent and want a property tour you need: > a matterport camera ($3000+) > a photographer who knows how to use it > matterport's monthly subscription > and you still get a glorified panorama, not real 3D this guy built a tool that takes your existing captures (camera files, drone shots, 360 photos, EVEN phone captures) and turns them into a real interactive 3D tour in one workflow no specialized hardware, no photographer, no switching between 5 different tools real estate agents, property managers, airbnb hosts, architects, interior designers all of them are paying hundreds per property for something this tool does from captures they already have the painful part was never understanding the end result. everyone knows what a 3D tour looks like. the pain is going from a pile of messy capture files to something usable without it turning into a technical project this solves that with one workflow you dump your files in and get a 3D tour out insane what you can do with vibe coding

Om Patel

158,397 次观看 • 5 个月前

This style of AI renovation video is helping RE agents and contractors close more deals (and it starts with a single photo). No staging company. No 3D rendering service. No $2K virtual staging package. Just one image and 10 minutes inside Calico AI. Here's how the system works: → Drop any room photo into the AI Room Renovator GPT → Choose a renovation style: modern, coastal, luxury → It hands you a perfectly tuned image prompt → Paste it inside Calico along with the original photo → Generate the renovated room in seconds → Open Kling 3.0 (same platform, no switching tools) — set original as start, renovation as end → Generate the animation — the room transforms before your eyes Before to after. In a video. From one photo. Drop that into a listing, an ad, or a client walkthrough and watch what happens. Comment "RENOVATE" and I'll send you the exact GPT link, prompts, and workflow (must be following so I can DM you!).

This style of AI renovation video is helping RE agents and contractors close more deals (and it starts with a single photo). No staging company. No 3D rendering service. No $2K virtual staging package. Just one image and 10 minutes inside Calico AI. Here's how the system works: → Drop any room photo into the AI Room Renovator GPT → Choose a renovation style: modern, coastal, luxury → It hands you a perfectly tuned image prompt → Paste it inside Calico along with the original photo → Generate the renovated room in seconds → Open Kling 3.0 (same platform, no switching tools) — set original as start, renovation as end → Generate the animation — the room transforms before your eyes Before to after. In a video. From one photo. Drop that into a listing, an ad, or a client walkthrough and watch what happens. Comment "RENOVATE" and I'll send you the exact GPT link, prompts, and workflow (must be following so I can DM you!).

David Roberts

34,851 次观看 • 6 个月前

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

66,769 次观看 • 2 年前