Static 3D generation isn't enough. We need assets ready... for animation. Our new #SIGGRAPH work, AniGen, takes a single image and generates the 3D shape, skeleton, and skinning weights all at once. Code is fully open-sourced! Kudos to Yihua and VAST AI Research 🧵(1/4)show more

Introducing 📦𝗔𝗿𝘁𝗶𝗟𝗮𝘁𝗲𝗻𝘁🔧 (SIGGRAPH Asia 2025) — a high-quality 3D... diffusion model that explicitly models object articulation, paving the way for richer, more realistic assets in embodied AI and simulation: – Generates fully articulated 3D objects – Physically plausible joints & motion – High-fidelity 3D Gaussian appearance – Supports generation from a single real image arXiv: Project: Code (coming soon):show more

1/ NVIDIA just open-sourced Cosmos 3 at GTC Taipei!... It's the first fully open "omnimodel" for physical AI - one model that understands the real world, predicts what happens next, and generates the actions a robot should take. Weights, code, datasets. All open. And this is really big. Lets dig into everything: 🧵show more

Generate FULLY CONTROLLABLE 3D assets from a SINGLE image,... locally on your PC. Made a 1-click launcher for the official Anigen Gradio app, and a dedicated viewer. Crazy this is now possible. What you're seeing here came from one image. Requires: NVIDIA GPU 6GB VRAMshow more

As announced in partnership with NVIDIA at CES, we’re... excited to introduce Stable Point Aware 3D (SPAR3D), setting a new standard in 3D generation. Ideal for running on NVIDIA RTX AI PCs, SPAR3D enables real-time editing and complete structure generation of 3D objects from a single image in under a second. You can download the weights on Hugging Face and code on GitHub, or access the model through the Stability AI API. Learn more here: (1/3)show more

Garment3DGen 3D Garment Stylization and Texture Generation We introduce... Garment3DGen a new method to synthesize 3D garment assets from a base mesh given a single input image as guidance. Our proposed approach allows users to generate 3D textured clothes based on bothshow more

I built a plugin to generate 3D assets directly... from Codex, using the latest update that introduces image generation. It generates an image, calls the Tripo3D API, then starts an MCP server and a local viewer directly inside Codex. It uses the paid Tripo3D API (12 free generations, 600 credits included). Everything is open source on my GitHub. Link below. Feel free to contribute. Room for improvement: skeleton/animation support and multi-provider 3D support.show more

We’ve seen humanoid robots walk around for a while,... but when will they actually help with useful tasks in daily life? The challenge here is the diversity and complexity of real-world scenes. Our new work tackles this problem via 3D visuomotor policy learning. Using data from only 1 scene, our Improved 3D Diffusion Policy (iDP3) enables a full-sized humanoid robot to autonomously pick&place objects, pour water, and wipe tables, in the wild open world. (and all these skills are useful, right?) Web: Fully open-sourced code:show more

DimensionX: Create Any 3D and 4D Scenes from a... Single Image with Controllable Video Diffusion TL;DR: Create 3/4DGS from Video Diffusion Note: Some first inference code released (not all yet). Contributions (cited): • We present DimensionX, a novel framework for generating photorealistic 3D and 4D scenes from only a single image using controllable video diffusion. • We propose ST-Director, which decouples the spatial and temporal priors in video diffusion models by learning (spatial and temporal) dimension-aware modules with our curated datasets. We further enhance the hybriddimension control with a training-free composition approach according to the essence of video diffusion denoising process. • To bridge the gap between video diffusion and real-world scenes, we design a trajectory-aware mechanism for 3D generation and an identity-preserving denoising approach for 4D generation, enabling more realistic and controllable scene synthesis. • Extensive experiments manifest that our DimensionX delivers superior performance in video, 3D, and 4D generation compared with baseline methods.show more

.Tencent Hy HY-World 2.0 has landed,fully OPEN SOURCE !... We kept hearing the same thing: world models look great, but you can't use the output. So we made one you can, real 3D assets you can edit, rearrange, and ship into production. Prompt to engine-ready 3D. For real this time： ✨Directly outputs editable, production-ready 3D assets ✨Seamlessly plugs into existing game pipelines ✨Multimodal input: text, image, videos 🪄Character mode: freely explore streets, buildings, and open worlds with realistic physics, collisions, and no time limits 🔗GitHub:show more

We are proud to announce our first contribution to... open source Computer Vision research! 🚀 Today, we’re publishing on Hugging Face a 1,000 3D Maps Dataset under CC-BY-NC license, designed to accelerate progress in 3D perception and spatial AI. Our way to give back to the global AI community and show how DePIN can make an impact. 🧵 👉show more

Drawing to 3d and animation. Here's how I did... this: @Vizcom_ai to make a more "rendered" version of the drawing. Leonardo ai image to image for the "3d" pass. Magnific ai for upscale/detail. Runway Gen-2 for animation. And yes, the soul of the drawing is kinda lost in translation 😬, but imagine once we're able to get that across.. I'm very impressed with how well the process carries over the various elements of the drawing. #ai #artshow more

AI has huge potential to change how we work... and live. That was clear at Montreal’s ALL IN Conference. But we also need to make sure our AI ecosystem can develop responsibly – and that’s what our new code of conduct does. Learn more about the code here:show more

This AI Agent generates 1000s of winning static ads... automatically. → Auto-generates 100+ unique image variations → AI writes custom prompts for each ad angle → OpenAI Image creates on-brand visuals in seconds → Links each image to specific ad copy automatically → Bulk generation with one checkbox → Editable prompts before image creation → Hosted images ready for immediate upload → Built in n8n + Airtable (zero coding) Perfect for ecomm operators, creative strategists, and Meta ad agencies. The workflow: 1. Drop in product URL 2. AI researches brand & audience 3. Generates 100+ ad copy variations 4. Creates 100+ custom image prompts 5. Produces 100+ static ad images All in under 5 minutes. >> No more creative bottlenecks. >> No more waiting on designers. Just static ads, at scale, on demand. Want access to the n8n template + Airtable database? Like + Comment "ADS" and I'll send it over.show more

We are excited to introduce Stable Fast 3D, Stability... AI’s latest breakthrough in 3D asset generation technology. This innovative model transforms a single input image into a detailed 3D asset in just 0.5 seconds, setting a new standard for speed and quality in the field of 3D reconstruction! Alongside this release, we’ve also published a technical report that highlights how we achieve fast inference speeds with reduced baked illumination and material parameters. 👾You can learn more and access the report here:show more

The #1 AI video model on LMArena isn't Sora.... It's Seedance 2.0, and it just went fully live with a feature nobody else has. You can feed it image + video + audio at once. Not one. All three. Here's why that changes everything: 🧵show more

Today, we are releasing Stable Video Diffusion, our first... foundation model for generative AI video based on the image model, Stable Diffusion. As part of this research preview, the code, weights, and research paper are now available. Additionally, today you can sign up for our waitlist to access a new upcoming web experience featuring a Text-To-Video interface. To access the model & sign up for our waitlist, visit our website here:show more

With Hunyuan3D World Model 1.0 now released and open-sourced,... we're excited to showcase the technical highlights behind this impressive innovation: ✅360° Panoramic Generation: Creates complete, immersive “world scenes”, far beyond localized views. ✅Explorable 3D Scene Generation: Generates diverse, spatially consistent 3D worlds from text/image for truly immersive exploration. ✅Interactive/Editable: Achieves separation of foreground objects, background terrain, ground, and sky, for seamless secondary editing. ✅Exportable Mesh: Generated scenes can be exported as 3D meshes for direct import into mainstream game engines and modeling software. ✅Industry-Leading SOTA Evaluation: Surpasses state-of-the-art open-source models in generation quality. As the industry's first open-source model for physical simulation and explorable world generation, Hunyuan3D World Model 1.0 aims to foster a collaborative community ecosystem with developers and enthusiasts. ✨ Try it now: 🤗 Hugging Face:show more

Wonderland: Navigating 3D Scenes from a Single Image Contributions:... • First, we introduce a representation for controllable 3D generation by leveraging the generative priors from camera-guided video diffusion models. Unlike image models, video diffusion models are trained on extensive video datasets. This enables them to capture comprehensive spatial relationships within scenes across multiple views and embed a form of "3D awareness" in their latent space, which allows us to maintain 3D consistency in novel view synthesis. • Second, to achieve controllable novel view generation, we empower video models with precise control over specified camera motions. We introduce a novel dual-branch conditioning mechanism that effectively incorporates desired diverse camera trajectories into the video diffusion model. This enables expansion of a single image into a multi-view consistent capture of a 3D scene with precise pose control. • Third, to achieve efficient 3D reconstruction, we directly transform video latents into 3DGS. We propose a novel latent-based large reconstruction model (LaLRM) that lifts video latents to 3D in a feed-forward manner. With this design, during inference, our model directly predicts 3DGS from a single input image, effectively aligning the generation and reconstruction tasks—and bridging image space and 3D space—through the video latent space. Compared with reconstructing scenes from images, the video latent space offers a 256× spatial-temporal reduction while retaining essential and consistent 3D structural details. Such a high degree of compression is crucial, as it allows the LaLRM to handle a wider range of 3D scenes within the reconstruction framework, with the same memory constraints.show more