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Introducing our newest tool from SideFX Labs, Regions from Image! Section out an input file into major shapes based off of dominant image colors, and use these shapes for masks, polygon extraction, and more! #Houdini SideFX #techart

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Danicka Oglesby

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14,614 görüntüleme • 2 yıl önce •via X (Twitter)

Bilim & TeknolojiEğitimSanat#Houdini#techart
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Kitae Kim2 yıl önce

@sidefxlabs @sidefx I love @sidefxlabs so much

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Paul Esteves2 yıl önce

@sidefxlabs @sidefx Very cool video! Lovely to see the tool and also a usecase!

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vincent thomas2 yıl önce

@sidefxlabs @sidefx Not sure why you are going by Cop context which is terribly slow and not stable. You could basicely do the same thing in less than 10 nodes and 5 min setup but well make lifer easier i guess for many!

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At Axis (💔), we had an incredibly useful tool that could automatically generate materials from a folder of textures. I decided to create something similar for my own projects and share it with the SideFX Houdini community—completely free! #3d #houdini #python #materialx
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At Axis (💔), we had an incredibly useful tool that could automatically generate materials from a folder of textures. I decided to create something similar for my own projects and share it with the SideFX Houdini community—completely free! #3d #houdini #python #materialx

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InstantDrag Improving Interactivity in Drag-based Image Editing discuss: Drag-based image editing has recently gained popularity for its interactivity and precision. However, despite the ability of text-to-image models to generate samples within a second, drag editing still lags behind due to the challenge of accurately reflecting user interaction while maintaining image content. Some existing approaches rely on computationally intensive per-image optimization or intricate guidance-based methods, requiring additional inputs such as masks for movable regions and text prompts, thereby compromising the interactivity of the editing process. We introduce InstantDrag, an optimization-free pipeline that enhances interactivity and speed, requiring only an image and a drag instruction as input. InstantDrag consists of two carefully designed networks: a drag-conditioned optical flow generator (FlowGen) and an optical flow-conditioned diffusion model (FlowDiffusion). InstantDrag learns motion dynamics for drag-based image editing in real-world video datasets by decomposing the task into motion generation and motion-conditioned image generation. We demonstrate InstantDrag's capability to perform fast, photo-realistic edits without masks or text prompts through experiments on facial video datasets and general scenes. These results highlight the efficiency of our approach in handling drag-based image editing, making it a promising solution for interactive, real-time applications.

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Go from weeks of visual effects work to instant visualization with Gen-4.5 Image to Video. Just drop an image in and ask for what you want. Looking for more tips? Check out our full collection of tutorial and guides at the link below.
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TurboEdit Instant text-based image editing discuss: We address the challenges of precise image inversion and disentangled image editing in the context of few-step diffusion models. We introduce an encoder based iterative inversion technique. The inversion network is conditioned on the input image and the reconstructed image from the previous step, allowing for correction of the next reconstruction towards the input image. We demonstrate that disentangled controls can be easily achieved in the few-step diffusion model by conditioning on an (automatically generated) detailed text prompt. To manipulate the inverted image, we freeze the noise maps and modify one attribute in the text prompt (either manually or via instruction based editing driven by an LLM), resulting in the generation of a new image similar to the input image with only one attribute changed. It can further control the editing strength and accept instructive text prompt. Our approach facilitates realistic text-guided image edits in real-time, requiring only 8 number of functional evaluations (NFEs) in inversion (one-time cost) and 4 NFEs per edit. Our method is not only fast, but also significantly outperforms state-of-the-art multi-step diffusion editing techniques.

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Exciting milestones in our generative AI research: Emu Video, which lets you create high quality videos from a text prompt, and Emu Edit, which enables detailed image editing based on your instructions. These new models are built on Emu, our foundation model for image generation and technology from them will underpin new creative features across our apps next year. Try it out: Emu Video: Emu Edit:
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Exciting milestones in our generative AI research: Emu Video, which lets you create high quality videos from a text prompt, and Emu Edit, which enables detailed image editing based on your instructions. These new models are built on Emu, our foundation model for image generation and technology from them will underpin new creative features across our apps next year. Try it out: Emu Video: Emu Edit:

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Introducing Higgsfield Marketing Studio powered by Seedance 2.0: a single workflow from product to finished ad. Bring a product image or paste a product link, use an avatar from your photo or build one inside the tool, and generate your ad end-to-end in 9 ad styles - UGC, unboxing, reviews, and more. New users get up to 70% off, with up to 14 days of Unlimited generations.* Start here:

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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.

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Introducing SDXL Turbo: A real-time text-to-image generation model. SDXL Turbo achieves state-of-the-art performance with a new distillation technology, enabling single-step image generation with unprecedented quality, reducing the required step count from 50 to just one. The code, research paper, and weights for non-commercial use are now available on our website. You can test SDXL Turbo on Stability AI’s image editing platform Clipdrop, with a beta demonstration of the real-time text-to-image generation capabilities. Learn more:

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RT to help someone in need of free services for image based abuse! If you or someone you know is a victim of image based sexual abuse go to Foundation Ra for free take down services, legal advice, therapy, law enforcement and so much more. I created Foundation Ra because these are the resources I wish I had when I became a victim of image based abuse and received no help or support from anywhere instead everyone wanted to profit off of me and kick me while I was already down. So I made sure I created a non profit with free and effective resources for anyone going through this today.

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make your CAD from Claude Code. insert image and let the mcp do its thing! this mcp can make compound shapes per sketch, do extrusions, insert fillets, create counterbore holes, non-feature script gears (working on a feature script based one), and some other cool things you’ll only know till you try lol! i forked hedless’ repo for the base mcp and added more functionalities so shoutout to hedless :) repo:
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make your CAD from Claude Code. insert image and let the mcp do its thing! this mcp can make compound shapes per sketch, do extrusions, insert fillets, create counterbore holes, non-feature script gears (working on a feature script based one), and some other cool things you’ll only know till you try lol! i forked hedless’ repo for the base mcp and added more functionalities so shoutout to hedless :) repo:

clarissa 🛋️

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📢 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

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Today, we're introducing SimFoundry, our real2sim2real framework at NVIDIA GEAR that automatically turns real-world scenes into simulation-ready worlds from a single image or video. Website: Paper: This work marks a major step for our team toward leveraging simulations and synthetic data for foundation model training and systematic policy evaluation at scale. Code will be open-sourced soon. Stay tuned!

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Introducing Document Extraction as an MCP Server ✂️📑 A huge use case for AI agents is being able to extract out items from a diverse set of complex documents in a repeatable manner - whether it’s legal contracts, invoices, financial statements, passports, and more. In this case, 🚫 prompting ChatGPT/Claude is too error-prone/not systematic. 💡We’ve created a plug-and-play MCP server around our core document extraction capabilities within LlamaCloud. 1. Build an extraction agent in LlamaCloud with a standardized schema. 2. Plug it as an MCP tool within ChatGPT/Claude. 3. For your next extraction job, get the outputs in a standardized format through the tool! Huge shoutout to Tuana and Logan Markewich for working on this - this is thanks to the latest updates to our llamacloud-mcp demo repo, which supports both extraction and retrieval as MCP tools. Repo: Sign up:

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🚀 Introducing InterDyn — our newly accepted CVPR work that explores controllable synthesis of interactive dynamics! Building upon powerful video diffusion models, InterDyn infers future motion and interactions directly from an input image and a dynamic control signal (e.g., a moving hand mask). Check out how we push the boundaries of intuitive physics with video generative models. Project page: Arxiv: #GenAI #AIGC #VideoGen #ML #ComputerVision #CVPR2025 🧵1/6

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[#CollovResearch] Excited to share our latest collaborative research with Yale University Berkeley AI Research(AI Lab) and NUS on DEdit( a cutting-edge method for precise and disentangled image editing ranked as the No.2 project, surpassing #flux1, incredible achievement for us! This milestone showcases our breakthrough in image editing technology and reflects the growing anticipation and recognition from the communityThis innovative approach focuses on making edits without altering or losing information from the original image, using a learnable prompt editing component for diffusion models. At #CollovLabs, we’ve extended #DEdit to make it accessible to users with diverse editing needs through an easy-to-use API. This enhanced pipeline improves segmentation and provides step-by-step guidance, allowing for seamless and faithful image editing via natural language input. Hugging Face : Full Paper: Paper authors: AosongFeng, Weikang Qiu, Rex Ying, Jinbin Bai,Xiao Zhang KC Zhou Zhen Dong Leandros Tassiulas

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Introducing Cline (formerly Claude Dev), an AI assistant that can use your CLI aNd Editor. v2.0 brings exciting updates: responses are now streamed into your editor, a cancel button for better control over tasks, a new XML-based tool calling prompt resulting in ~40% fewer requests per task + better performance from non-Claude models, search and use any model on OpenRouter (search “free” for no-cost options). More details and benchmarks in the release notes:

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