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⚡🎉 We are thrilled to introduce VORTEX, an AI-powered computational framework for predicting 3D Spatial Transcriptomics (ST) using 3D tissue images and minimal 2D ST! 🧬 By combining cutting-edge 3D non-destructive tissue imaging with AI, VORTEX imputes the 3D molecular landscape of large tissue samples in a cost-effective and... scalable manner. 🧠💡Our approach: By pretraining on diverse 3D morphology–2D transcriptomic pairs from heterogeneous tissue samples, and then fine-tuning on minimal 2D ST data from a volume of interest, VORTEX leverages both generic tissue-specific and sample-specific morphomolecular correlates to predict 3D ST. Congratulations to our superstar co-leads Cristina Almagro Pérez and Andrew H. Song, this was an exciting collaboration with Jonathan Liu Sizun Jiang Ali Bashashati. Preprint: Demo: Read the excellent blog from our superstar grad student Cristina Almagro Pérez: Also see our previous work on 3D Computational Pathology from Andrew H. Song published in Cell last year: Stay tuned for more to come.show more

Faisal Mahmood

6,989 subscribers

17,991 次观看 • 1 年前 •via X (Twitter)

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Joe Yeong1 年前

hi Faisal, welcome to Asia in June. :) texted u in both twitter and Linkedin. :)

SCAI 的头像

SCAI1 年前

💡 Take your interventional cardiology career to the next level at SCAI 2025 Scientific Sessions. Join us on May 1-3, in Washington, D.C. Three days of innovative science, practical education, and world-class networking await you.🌟 Secure your spot today.

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DrNeil1 年前

I think there should be a artificial general intelligence medicine ai or branched or field based ai system which learn and self imporve.

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⚡️📣👇Tremendously excited to share our new Cell article, where we develop TriPath, a method for analyzing 3D pathology samples using weakly supervised AI. Article: TriPath enables 3D computational pathology via 3D multiple instance learning allowing AI models to capture intricate morphological details from pathology volumes. Code: Blog post: Tested on two different imaging modalities, and patient cohorts from two institutions. Our superstar Andrew H. Song put in a monumental effort of leading the study, in a fantastic collaboration with Jonathan Liu at University of Washington . Interesting aspects: - Utilizing the whole tissue volume and leveraging 3D deep learning enable superior risk prediction performance compared to 2D deep learning baselines based on a few sampled tissue sections that emulate standard clinical practice. This indicates TriPath can harness additional information provided by 3D tissue morphology. - The performance is also superior to clinical baselines from a reader study that involved six expert pathologists. - The morphologically heterogeneous tissue volume could lead to opposing patient-level outcome predictions, dependent on which portion of the tissue volume is used. This concurs with current clinical literature warning that tissue sampling bias can lead to misdiagnosis. Some limitations: - While the 3D pathology cohort size is unprecedented, it is smaller than typical 2D pathology cohorts. Further large-scale studies will be required for validation. Nevertheless, we believe that this study will initiate a positive cycle, encouraging academic institutions and pharmaceutical companies to contribute large banks of human tissue blocks with paired clinical outcomes, thus speeding up advancements in 3D computational pathology. Concluding insights: We believe that 3D pathology is just around the corner - It has the huge potential to not only augment/improve the current clinical practice centered around 2D examination of human tissue, but also help reveal novel biomarkers for prognosis and therapeutic response.. Harvard Medical School Harvard Data Science Initiative Mass General Brigham Broad Institute

⚡️📣👇Tremendously excited to share our new Cell article, where we develop TriPath, a method for analyzing 3D pathology samples using weakly supervised AI. Article: TriPath enables 3D computational pathology via 3D multiple instance learning allowing AI models to capture intricate morphological details from pathology volumes. Code: Blog post: Tested on two different imaging modalities, and patient cohorts from two institutions. Our superstar Andrew H. Song put in a monumental effort of leading the study, in a fantastic collaboration with Jonathan Liu at University of Washington . Interesting aspects: - Utilizing the whole tissue volume and leveraging 3D deep learning enable superior risk prediction performance compared to 2D deep learning baselines based on a few sampled tissue sections that emulate standard clinical practice. This indicates TriPath can harness additional information provided by 3D tissue morphology. - The performance is also superior to clinical baselines from a reader study that involved six expert pathologists. - The morphologically heterogeneous tissue volume could lead to opposing patient-level outcome predictions, dependent on which portion of the tissue volume is used. This concurs with current clinical literature warning that tissue sampling bias can lead to misdiagnosis. Some limitations: - While the 3D pathology cohort size is unprecedented, it is smaller than typical 2D pathology cohorts. Further large-scale studies will be required for validation. Nevertheless, we believe that this study will initiate a positive cycle, encouraging academic institutions and pharmaceutical companies to contribute large banks of human tissue blocks with paired clinical outcomes, thus speeding up advancements in 3D computational pathology. Concluding insights: We believe that 3D pathology is just around the corner - It has the huge potential to not only augment/improve the current clinical practice centered around 2D examination of human tissue, but also help reveal novel biomarkers for prognosis and therapeutic response.. Harvard Medical School Harvard Data Science Initiative Mass General Brigham Broad Institute

Faisal Mahmood

65,541 次观看 • 2 年前

LLaVA-3D A Simple yet Effective Pathway to Empowering LMMs with 3D-awareness Recent advancements in Large Multimodal Models (LMMs) have greatly enhanced their proficiency in 2D visual understanding tasks, enabling them to effectively process and understand images and videos. However, the development of LMMs with 3D-awareness for 3D scene understanding has been hindered by the lack of large-scale 3D vision-language datasets and powerful 3D encoders. In this paper, we introduce a simple yet effective framework called LLaVA-3D. Leveraging the strong 2D understanding priors from LLaVA, our LLaVA-3D efficiently adapts LLaVA for 3D scene understanding without compromising 2D understanding capabilities. To achieve this, we employ a simple yet effective representation, 3D Patch, which connects 2D CLIP patch features with their corresponding positions in 3D space. By integrating the 3D Patches into 2D LMMs and employing joint 2D and 3D vision-language instruction tuning, we establish a unified architecture for both 2D image understanding and 3D scene understanding. Experimental results show that LLaVA-3D converges 3.5x faster than existing 3D LMMs when trained on 3D vision-language datasets. Moreover, LLaVA-3D not only achieves state-of-the-art performance across various 3D tasks but also maintains comparable 2D image understanding and vision-language conversation capabilities with LLaVA.

LLaVA-3D A Simple yet Effective Pathway to Empowering LMMs with 3D-awareness Recent advancements in Large Multimodal Models (LMMs) have greatly enhanced their proficiency in 2D visual understanding tasks, enabling them to effectively process and understand images and videos. However, the development of LMMs with 3D-awareness for 3D scene understanding has been hindered by the lack of large-scale 3D vision-language datasets and powerful 3D encoders. In this paper, we introduce a simple yet effective framework called LLaVA-3D. Leveraging the strong 2D understanding priors from LLaVA, our LLaVA-3D efficiently adapts LLaVA for 3D scene understanding without compromising 2D understanding capabilities. To achieve this, we employ a simple yet effective representation, 3D Patch, which connects 2D CLIP patch features with their corresponding positions in 3D space. By integrating the 3D Patches into 2D LMMs and employing joint 2D and 3D vision-language instruction tuning, we establish a unified architecture for both 2D image understanding and 3D scene understanding. Experimental results show that LLaVA-3D converges 3.5x faster than existing 3D LMMs when trained on 3D vision-language datasets. Moreover, LLaVA-3D not only achieves state-of-the-art performance across various 3D tasks but also maintains comparable 2D image understanding and vision-language conversation capabilities with LLaVA.

AK

41,736 次观看 • 1 年前

3D-LLM: Injecting the 3D World into Large Language Models paper page: Large language models (LLMs) and Vision-Language Models (VLMs) have been proven to excel at multiple tasks, such as commonsense reasoning. Powerful as these models can be, they are not grounded in the 3D physical world, which involves richer concepts such as spatial relationships, affordances, physics, layout, and so on. In this work, we propose to inject the 3D world into large language models and introduce a whole new family of 3D-LLMs. Specifically, 3D-LLMs can take 3D point clouds and their features as input and perform a diverse set of 3D-related tasks, including captioning, dense captioning, 3D question answering, task decomposition, 3D grounding, 3D-assisted dialog, navigation, and so on. Using three types of prompting mechanisms that we design, we are able to collect over 300k 3D-language data covering these tasks. To efficiently train 3D-LLMs, we first utilize a 3D feature extractor that obtains 3D features from rendered multi- view images. Then, we use 2D VLMs as our backbones to train our 3D-LLMs. By introducing a 3D localization mechanism, 3D-LLMs can better capture 3D spatial information. Experiments on ScanQA show that our model outperforms state-of-the-art baselines by a large margin (e.g., the BLEU-1 score surpasses state-of-the-art score by 9%). Furthermore, experiments on our held-in datasets for 3D captioning, task composition, and 3D-assisted dialogue show that our model outperforms 2D VLMs. Qualitative examples also show that our model could perform more tasks beyond the scope of existing LLMs and VLMs.

3D-LLM: Injecting the 3D World into Large Language Models paper page: Large language models (LLMs) and Vision-Language Models (VLMs) have been proven to excel at multiple tasks, such as commonsense reasoning. Powerful as these models can be, they are not grounded in the 3D physical world, which involves richer concepts such as spatial relationships, affordances, physics, layout, and so on. In this work, we propose to inject the 3D world into large language models and introduce a whole new family of 3D-LLMs. Specifically, 3D-LLMs can take 3D point clouds and their features as input and perform a diverse set of 3D-related tasks, including captioning, dense captioning, 3D question answering, task decomposition, 3D grounding, 3D-assisted dialog, navigation, and so on. Using three types of prompting mechanisms that we design, we are able to collect over 300k 3D-language data covering these tasks. To efficiently train 3D-LLMs, we first utilize a 3D feature extractor that obtains 3D features from rendered multi- view images. Then, we use 2D VLMs as our backbones to train our 3D-LLMs. By introducing a 3D localization mechanism, 3D-LLMs can better capture 3D spatial information. Experiments on ScanQA show that our model outperforms state-of-the-art baselines by a large margin (e.g., the BLEU-1 score surpasses state-of-the-art score by 9%). Furthermore, experiments on our held-in datasets for 3D captioning, task composition, and 3D-assisted dialogue show that our model outperforms 2D VLMs. Qualitative examples also show that our model could perform more tasks beyond the scope of existing LLMs and VLMs.

AK

249,708 次观看 • 3 年前

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

305,663 次观看 • 3 年前

We are thrilled to share that our first paper from my new lab, Spateo ( for spatiotemporal modeling of molecular holograms, is now online in Cell: Spateo is a comprehensive analytical framework for 3D whole-embryo spatiotemporal modeling. Its advanced features include: • 3D alignment and reconstruction at the whole-mouse-embryo scale (see the animation). • 3D spatial domain digitization and cell-cell communication analysis to understand spatial gene expression gradients and both inter- and intracellular communication. • 3D morphometric and volumetric analyses along with 3D morphogenesis vector field modeling to quantify dynamics such as surface area, volume, and cell density across organs, and to dissect the interplay between morphogenesis factors and cell migration. • A “Google Earth”-like browser, Spateo-viewer ( and for interactive and intuitive exploration of 3D spatial data. • Additional features, such as RNA signal-based single-cell segmentation. We are also honored that Nature “News and Views” has highlighted this work as well: This is really an amazing outcome after two years' heroic revision process that rewrite the entire paper using a new data ( for whole mouse embryos.

We are thrilled to share that our first paper from my new lab, Spateo ( for spatiotemporal modeling of molecular holograms, is now online in Cell: Spateo is a comprehensive analytical framework for 3D whole-embryo spatiotemporal modeling. Its advanced features include: • 3D alignment and reconstruction at the whole-mouse-embryo scale (see the animation). • 3D spatial domain digitization and cell-cell communication analysis to understand spatial gene expression gradients and both inter- and intracellular communication. • 3D morphometric and volumetric analyses along with 3D morphogenesis vector field modeling to quantify dynamics such as surface area, volume, and cell density across organs, and to dissect the interplay between morphogenesis factors and cell migration. • A “Google Earth”-like browser, Spateo-viewer ( and for interactive and intuitive exploration of 3D spatial data. • Additional features, such as RNA signal-based single-cell segmentation. We are also honored that Nature “News and Views” has highlighted this work as well: This is really an amazing outcome after two years' heroic revision process that rewrite the entire paper using a new data ( for whole mouse embryos.

evo-devo

58,343 次观看 • 1 年前

ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation propose to model the 3D parameter as a random variable instead of a constant as in SDS and present variational score distillation (VSD), a principled particle-based variational framework to explain and address the aforementioned issues in text-to-3D generation. We show that SDS is a special case of VSD and leads to poor samples with both small and large CFG weights. In comparison, VSD works well with various CFG weights as ancestral sampling from diffusion models and simultaneously improves the diversity and sample quality with a common CFG weight (i.e., 7.5). We further present various improvements in the design space for text-to-3D such as distillation time schedule and density initialization, which are orthogonal to the distillation algorithm yet not well explored. Our overall approach, dubbed ProlificDreamer, can generate high rendering resolution (i.e., 512times512) and high-fidelity NeRF with rich structure and complex effects (e.g., smoke and drops). Further, initialized from NeRF, meshes fine-tuned by VSD are meticulously detailed and photo-realistic paper page:

ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation propose to model the 3D parameter as a random variable instead of a constant as in SDS and present variational score distillation (VSD), a principled particle-based variational framework to explain and address the aforementioned issues in text-to-3D generation. We show that SDS is a special case of VSD and leads to poor samples with both small and large CFG weights. In comparison, VSD works well with various CFG weights as ancestral sampling from diffusion models and simultaneously improves the diversity and sample quality with a common CFG weight (i.e., 7.5). We further present various improvements in the design space for text-to-3D such as distillation time schedule and density initialization, which are orthogonal to the distillation algorithm yet not well explored. Our overall approach, dubbed ProlificDreamer, can generate high rendering resolution (i.e., 512times512) and high-fidelity NeRF with rich structure and complex effects (e.g., smoke and drops). Further, initialized from NeRF, meshes fine-tuned by VSD are meticulously detailed and photo-realistic paper page:

AK

46,151 次观看 • 3 年前

Another city. Another full 3D Gaussian Splat reconstruction. This entire neighborhood was captured while walking with an Insta360 X5, then processed with our GPU pipeline and transformed into a high-fidelity, machine-readable 3D environment with Over the Reality. What you’re seeing is not an isolated virtual world. It’s a 3D digital twin of a real-world location, built from structured spatial data where every building, street, and detail can become usable by AI, robotics, and XR. And it’s happening at scale, across cities in more than 180 countries every day. Have an Insta360 X5 and want to become a mapper? Apply here: Want to build a 3D Gaussian Splat from a video you already recorded? Do it for free at powered by Over the Reality.

Another city. Another full 3D Gaussian Splat reconstruction. This entire neighborhood was captured while walking with an Insta360 X5, then processed with our GPU pipeline and transformed into a high-fidelity, machine-readable 3D environment with Over the Reality. What you’re seeing is not an isolated virtual world. It’s a 3D digital twin of a real-world location, built from structured spatial data where every building, street, and detail can become usable by AI, robotics, and XR. And it’s happening at scale, across cities in more than 180 countries every day. Have an Insta360 X5 and want to become a mapper? Apply here: Want to build a 3D Gaussian Splat from a video you already recorded? Do it for free at powered by Over the Reality.

Over the Reality 🌐

400,097 次观看 • 2 个月前

PantheonOS allows any biologist to perform complex data analyses of emerging single cell, multi-omics and spatial transcriptomics datasets end to end through AI agent and human collaboration. We are releasing six replays of use cases trajectories. Each "trajectory" is a complete end-to-end run, from prompt to analysis, to figures, and finally to report, and in fully inspectable and reproducible manner. See the six user cases in our PantheonOS Gallery: 1. 3D mouse embryo analysis: Tangram deconvolution and PyVista-based 3D visualization of E6 mouse embryo spatial transcriptomics data 2. 3D human fetal heart analysis: Spatial mapping of heart disease gene patterns using MERFISH data 3. Multi-omics spatial integration: Single cell multi-omics-to-spatial mapping with MOSCOT optimal transport 4. Gene panel design: 1000-plex immune-oncology MERFISH gene panel optimization 5. Cell segmentation benchmarking: Comparative evaluation of Cellpose-SAM, InstanSeg, StarDist, and other tools 6. Spatial disease biology: Ligand–receptor analysis of disease-associated tissue microenvironments We would love to hear how you can use PantheonOS for your research! Got an interesting agent run of your own and want to share? In the Pantheon UI, click Export Bundle (top-right of any chat) to package the full trajectory — chat history, code, figures, report — then submit it here:

PantheonOS allows any biologist to perform complex data analyses of emerging single cell, multi-omics and spatial transcriptomics datasets end to end through AI agent and human collaboration. We are releasing six replays of use cases trajectories. Each "trajectory" is a complete end-to-end run, from prompt to analysis, to figures, and finally to report, and in fully inspectable and reproducible manner. See the six user cases in our PantheonOS Gallery: 1. 3D mouse embryo analysis: Tangram deconvolution and PyVista-based 3D visualization of E6 mouse embryo spatial transcriptomics data 2. 3D human fetal heart analysis: Spatial mapping of heart disease gene patterns using MERFISH data 3. Multi-omics spatial integration: Single cell multi-omics-to-spatial mapping with MOSCOT optimal transport 4. Gene panel design: 1000-plex immune-oncology MERFISH gene panel optimization 5. Cell segmentation benchmarking: Comparative evaluation of Cellpose-SAM, InstanSeg, StarDist, and other tools 6. Spatial disease biology: Ligand–receptor analysis of disease-associated tissue microenvironments We would love to hear how you can use PantheonOS for your research! Got an interesting agent run of your own and want to share? In the Pantheon UI, click Export Bundle (top-right of any chat) to package the full trajectory — chat history, code, figures, report — then submit it here:

evo-devo

13,318 次观看 • 2 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

66,435 次观看 • 2 年前

Introducing the 10Ka Team 🤝 We've curated a team of exceptionally skilled individuals, each a trailblazer in their respective fields. Together, we possess all the necessary ingredients to realize our vision of becoming the premier project for digital fashion. Let's dive in 👇🏼 Diana Cañas: Breaking Barriers: Diana Cañas Leads the Way in NFT and Digital Fashion Empowerment. As the founder and CEO of 10ka NFT, Diana has taken the industry by storm, brilliantly combining her love for fashion with cutting-edge technologies like NFTs and NFCs. This unique fusion enables her to push the boundaries of the digital fashion realm. Ronin Studio: Ronin is a versatile creative studio specializing in corporate identity and character creation, along with UI/UX design. “Driven by a dedication to emerging trends and artistic/technological innovation, we draw inspiration from both the past and the future of design. Our approach is inherently disruptive, always encouraging us to think beyond conventional boundaries.” Tripton: We are a dynamic creative studio with expertise in 3D production, Visual Shows, and Virtual and Augmented Reality. “Fueled by a passion for pioneering technologies and groundbreaking ideas, we draw inspiration from eminent artists across advertising design, 3D animation, and the broader audiovisual realm.” Virtuozo: Virtuozo is a studio renowned for crafting immersive 3D worlds and captivating virtual experiences. At the helm is our lead 3D generalist, a virtuoso in every sense of the word. With mastery over the craft, he meticulously crafts realistic and aesthetically pleasing 3D models, textures, and animations using state-of-the-art technology. Cristian Torrijos: Blockchain Developer (Solidity, Smart Contracts, Web3) and expert in PHP, MySQL, and ReactJS. “Specializing in the development of Web3 and Web2 projects, we ensure the quality and longevity of our creations, delivering projects that are not only innovative but also enduring.” Domi Vakero: Digital Artist from the South of Spain proficient in 2D, 3D, and AI. With extensive expertise in the illustration and modeling of both 2D and 3D characters, we bring visions to life with creativity and precision. This is just some of the extensive team behind 10KA. We are dedicated to building & becoming one of the leading projects on Ethereum 🫡

Introducing the 10Ka Team 🤝 We've curated a team of exceptionally skilled individuals, each a trailblazer in their respective fields. Together, we possess all the necessary ingredients to realize our vision of becoming the premier project for digital fashion. Let's dive in 👇🏼 Diana Cañas: Breaking Barriers: Diana Cañas Leads the Way in NFT and Digital Fashion Empowerment. As the founder and CEO of 10ka NFT, Diana has taken the industry by storm, brilliantly combining her love for fashion with cutting-edge technologies like NFTs and NFCs. This unique fusion enables her to push the boundaries of the digital fashion realm. Ronin Studio: Ronin is a versatile creative studio specializing in corporate identity and character creation, along with UI/UX design. “Driven by a dedication to emerging trends and artistic/technological innovation, we draw inspiration from both the past and the future of design. Our approach is inherently disruptive, always encouraging us to think beyond conventional boundaries.” Tripton: We are a dynamic creative studio with expertise in 3D production, Visual Shows, and Virtual and Augmented Reality. “Fueled by a passion for pioneering technologies and groundbreaking ideas, we draw inspiration from eminent artists across advertising design, 3D animation, and the broader audiovisual realm.” Virtuozo: Virtuozo is a studio renowned for crafting immersive 3D worlds and captivating virtual experiences. At the helm is our lead 3D generalist, a virtuoso in every sense of the word. With mastery over the craft, he meticulously crafts realistic and aesthetically pleasing 3D models, textures, and animations using state-of-the-art technology. Cristian Torrijos: Blockchain Developer (Solidity, Smart Contracts, Web3) and expert in PHP, MySQL, and ReactJS. “Specializing in the development of Web3 and Web2 projects, we ensure the quality and longevity of our creations, delivering projects that are not only innovative but also enduring.” Domi Vakero: Digital Artist from the South of Spain proficient in 2D, 3D, and AI. With extensive expertise in the illustration and modeling of both 2D and 3D characters, we bring visions to life with creativity and precision. This is just some of the extensive team behind 10KA. We are dedicated to building & becoming one of the leading projects on Ethereum 🫡

10KA👟

39,253 次观看 • 2 年前

We’re thrilled to share that our MERFISH+ preprint is now live on bioRxiv!👉 In this work, the Bintu and Zhu labs (UCSD) developed MERFISH+, a next-generation spatial genomics platform that combines genome-wide RNA and epigenetic imaging over a large field of view. By introducing acrydite-modified probes covalently anchored to hydrogels, MERFISH+ achieves remarkable imaging stability and enables >1,800-gene, multi-modal, and multi-month experiments. With this platform, they, together with the Chi lab at UCSD, profiled a whole developing human heart at 12 post-conception week with merely two slides, resulting in a total of 53 slides, 3.1 million single cells and more than 30 cell types. Building upon our previous 3D reconstruction and modeling framework, Spateo ( we reconstruct the 3D human heart that nicely captures the anatomical structure of the heart, including the intricate vasculature network. Sophisticated analyses provide a holistic view of an entire organ and enable systematic characterization of 3D cellular neighborhoods and transcriptional gradients of substructures such as the descending arteries. Furthermore, using a generative integration framework for spatial multimodal data (Spateo-VI), we harmonized these MERFISH+ transcriptomic and chromatin data to reconstruct a 3D spatially-resolved multi-omics atlas of the developing human heart, shared at and MERFISH+ thus sets a new standard for large-format, multi-omic spatial profiling, enabling holistic, 3D characterization of organs at subcellular resolution. Huge congratulations to first authors Colin Kern, qingquan Zhang, Yifan Lu , and Jacqueline Eschbach, and to all collaborators from the Bintu, Zhu, Chi, and Qiu labs for this amazing team effort. Thanks for your diligence, creativity, and hard work on this project. We’re grateful for support from Arc Institute and our generous donors. Our lab is expanding—if you’re excited about building the next generation of single-cell and spatial genomics techniques and predictive single cell and spatial foundation models, we’re hiring! If you are interested, please reach out to me via direct message or email at xiaojie@stanford.edu. We are excited for any potential collaborations along this line of research in Stanford, UCSF and Berkeley and other labs as well.

We’re thrilled to share that our MERFISH+ preprint is now live on bioRxiv!👉 In this work, the Bintu and Zhu labs (UCSD) developed MERFISH+, a next-generation spatial genomics platform that combines genome-wide RNA and epigenetic imaging over a large field of view. By introducing acrydite-modified probes covalently anchored to hydrogels, MERFISH+ achieves remarkable imaging stability and enables >1,800-gene, multi-modal, and multi-month experiments. With this platform, they, together with the Chi lab at UCSD, profiled a whole developing human heart at 12 post-conception week with merely two slides, resulting in a total of 53 slides, 3.1 million single cells and more than 30 cell types. Building upon our previous 3D reconstruction and modeling framework, Spateo ( we reconstruct the 3D human heart that nicely captures the anatomical structure of the heart, including the intricate vasculature network. Sophisticated analyses provide a holistic view of an entire organ and enable systematic characterization of 3D cellular neighborhoods and transcriptional gradients of substructures such as the descending arteries. Furthermore, using a generative integration framework for spatial multimodal data (Spateo-VI), we harmonized these MERFISH+ transcriptomic and chromatin data to reconstruct a 3D spatially-resolved multi-omics atlas of the developing human heart, shared at and MERFISH+ thus sets a new standard for large-format, multi-omic spatial profiling, enabling holistic, 3D characterization of organs at subcellular resolution. Huge congratulations to first authors Colin Kern, qingquan Zhang, Yifan Lu , and Jacqueline Eschbach, and to all collaborators from the Bintu, Zhu, Chi, and Qiu labs for this amazing team effort. Thanks for your diligence, creativity, and hard work on this project. We’re grateful for support from Arc Institute and our generous donors. Our lab is expanding—if you’re excited about building the next generation of single-cell and spatial genomics techniques and predictive single cell and spatial foundation models, we’re hiring! If you are interested, please reach out to me via direct message or email at [email protected]. We are excited for any potential collaborations along this line of research in Stanford, UCSF and Berkeley and other labs as well.

evo-devo

42,208 次观看 • 8 个月前

🚀 We’re hiring! Staff Scientist / Postdoc – Tissue Clearing & 3D Image Analysis (m/f/d) (LMU Munich) Are you a great fit, or do you know someone outstanding, please reach out 🔁 If you want to at the frontier of whole-organ / whole-body 3D imaging, and help generate truly beautiful datasets that drive major biological discoveries and therapeutic development, see below ✨ We’re building the next-generation pipeline for tissue clearing + light-sheet microscopy + quantitative 3D analysis in the SyNergy Excellence Cluster (Mesoscale Hub) and we’re looking for someone excited to push this forward with us. 🧠🔬📈 🎥 I’m also attaching a short video showing the kind of high-quality imaging and datasets you’d be working with. What you’ll do 🛠️ 🔹 Lead and evolve tissue clearing + light-sheet workflows across collaborative SyNergy projects 🔹 Turn complex 3D datasets into robust quantitative insights (visualization, atlas registration, readouts) 🔹 Develop new methods and analysis pipelines together with our AI team 🤖 🔹 Maintain and optimize cutting-edge light-sheet systems (optional: support animal license writing) What we’re looking for 🎯 ✅ Strong hands-on experience in tissue clearing and/or fluorescence microscopy ✅ Solid experience with light-sheet microscopy and 3D imaging workflows ✅ Familiarity with 3D tools like Imaris / arivis Vision4D, stitching (e.g., BigStitcher), and quantitative analysis in cleared tissues ✅ Service mindset, great organization, and strong scientific English How to apply 📩 Apply via the LMU Klinikum online application form Please also send your application to: farida.hellal@med.uni-muenchen.de CC: ali.ertuerk@med.uni-muenchen.de 📎 Include one PDF: short cover letter, CV, 2–3 referees, and earliest start date. 📍 Campus Großhadern (Munich) and Helmholtz Munich | 🕒 Full-time | 📅 Start: 01 January 2026 If you love high-quality imaging, cutting-edge biology, and building something that will matter, we’d love to hear from you. 🌍✨ #hiring #StaffScientist #Postdoc #TissueClearing #LightSheetMicroscopy #ImageAnalysis #SpatialBiology #Neuroscience #SyNergy #LMU #Munich

🚀 We’re hiring! Staff Scientist / Postdoc – Tissue Clearing & 3D Image Analysis (m/f/d) (LMU Munich) Are you a great fit, or do you know someone outstanding, please reach out 🔁 If you want to at the frontier of whole-organ / whole-body 3D imaging, and help generate truly beautiful datasets that drive major biological discoveries and therapeutic development, see below ✨ We’re building the next-generation pipeline for tissue clearing + light-sheet microscopy + quantitative 3D analysis in the SyNergy Excellence Cluster (Mesoscale Hub) and we’re looking for someone excited to push this forward with us. 🧠🔬📈 🎥 I’m also attaching a short video showing the kind of high-quality imaging and datasets you’d be working with. What you’ll do 🛠️ 🔹 Lead and evolve tissue clearing + light-sheet workflows across collaborative SyNergy projects 🔹 Turn complex 3D datasets into robust quantitative insights (visualization, atlas registration, readouts) 🔹 Develop new methods and analysis pipelines together with our AI team 🤖 🔹 Maintain and optimize cutting-edge light-sheet systems (optional: support animal license writing) What we’re looking for 🎯 ✅ Strong hands-on experience in tissue clearing and/or fluorescence microscopy ✅ Solid experience with light-sheet microscopy and 3D imaging workflows ✅ Familiarity with 3D tools like Imaris / arivis Vision4D, stitching (e.g., BigStitcher), and quantitative analysis in cleared tissues ✅ Service mindset, great organization, and strong scientific English How to apply 📩 Apply via the LMU Klinikum online application form Please also send your application to: [email protected] CC: [email protected] 📎 Include one PDF: short cover letter, CV, 2–3 referees, and earliest start date. 📍 Campus Großhadern (Munich) and Helmholtz Munich | 🕒 Full-time | 📅 Start: 01 January 2026 If you love high-quality imaging, cutting-edge biology, and building something that will matter, we’d love to hear from you. 🌍✨ #hiring #StaffScientist #Postdoc #TissueClearing #LightSheetMicroscopy #ImageAnalysis #SpatialBiology #Neuroscience #SyNergy #LMU #Munich

Ali Max Erturk

14,751 次观看 • 7 个月前

📢📢 𝐀𝐯𝐚𝐭𝟑𝐫 📢📢 Avat3r creates high-quality 3D head avatars from just a few input images in a single forward pass with a new dynamic 3DGS reconstruction model. Video: Project: Our core idea is to make Gaussian Reconstruction Models animatable. We find that a simple cross-attention to an expression code sequence is already sufficient to model complex facial expressions. We then incorporate position maps from DUSt3R and feature maps from Sapiens to facilitate the prediction task. While DUSt3R's position maps act as a pixel-aligned initialization for the Gaussians' positions, the Sapiens feature maps help the cross-view transformer to match corresponding image tokens in the 4 input images. One major challenge in creating a 3D head avatar from smartphone images comes from inconsistent facial expressions when the subject could not remain perfectly static during the capture. We eliminate this static requirement by simply showing our model input images with different facial expressions during training. This technique makes our model robust to inconsistent input images later on. Finally, we show that despite the model has been trained with 4 input images, one can even create a 3D head avatar when only a single image is available. To achieve this, we employ a pre-trained 3D GAN to lift the single image to 3D and then render the 4 input images for our model. This allows us to create 3D head avatars from single images and even highly out-of-distribution examples like AI generated faces, paintings or statues. Great work by Tobias Kirschstein from his internship at Meta with Javier Romero, Artem Sevastopolsky, and Shunsuke Saito

Matthias Niessner

74,763 次观看 • 1 年前

📢 NEURALAI X 🤩 NeuralAI is extraordinarily excited to reveal that we have started working closely with Akash Network, the world's premier decentralized compute marketplace! ✔️ The offerings of align perfectly with the vision and roadmap of NeuralAI, making this collaboration extremely significant for NeuralAI. For a start, NeuralAI will run inference on Akash based on our 3D #AI model and prepare TAO miners for reward-yielding subnets. NeuralAI will also leverage Akash's powerful GPUs to enhance our #AI #model training. Currently, provides us with 56 powerful A6000 GPUs, scalable on demand. ✔️ NeuralAI also gets highly competitive pricing from Akash Network, significantly lower than any other compute provider in the world. The lower pricing is possible partially thanks to the crypto-driven incentives within Akash Network, making it an excellent opportunity for NeuralAI to leverage. The synergy with Akash Network is a big leap forward, fueling our mission of becoming the #1 AI-driven 3D asset generator subnet on Bittensor. ✔️ We look forward to our close work with , we are certain with their support we will be able to secure a unique edge in the market! Stay tuned for exciting updates and developments.

📢 NEURALAI X 🤩 NeuralAI is extraordinarily excited to reveal that we have started working closely with Akash Network, the world's premier decentralized compute marketplace! ✔️ The offerings of align perfectly with the vision and roadmap of NeuralAI, making this collaboration extremely significant for NeuralAI. For a start, NeuralAI will run inference on Akash based on our 3D #AI model and prepare TAO miners for reward-yielding subnets. NeuralAI will also leverage Akash's powerful GPUs to enhance our #AI #model training. Currently, provides us with 56 powerful A6000 GPUs, scalable on demand. ✔️ NeuralAI also gets highly competitive pricing from Akash Network, significantly lower than any other compute provider in the world. The lower pricing is possible partially thanks to the crypto-driven incentives within Akash Network, making it an excellent opportunity for NeuralAI to leverage. The synergy with Akash Network is a big leap forward, fueling our mission of becoming the #1 AI-driven 3D asset generator subnet on Bittensor. ✔️ We look forward to our close work with , we are certain with their support we will be able to secure a unique edge in the market! Stay tuned for exciting updates and developments.

NeuralAI

241,287 次观看 • 2 年前

PointOdyssey: A Large-Scale Synthetic Dataset for Long-Term Point Tracking paper page: introduce PointOdyssey, a large-scale synthetic dataset, and data generation framework, for the training and evaluation of long-term fine-grained tracking algorithms. Our goal is to advance the state-of-the-art by placing emphasis on long videos with naturalistic motion. Toward the goal of naturalism, we animate deformable characters using real-world motion capture data, we build 3D scenes to match the motion capture environments, and we render camera viewpoints using trajectories mined via structure-from-motion on real videos. We create combinatorial diversity by randomizing character appearance, motion profiles, materials, lighting, 3D assets, and atmospheric effects. Our dataset currently includes 104 videos, averaging 2,000 frames long, with orders of magnitude more correspondence annotations than prior work. We show that existing methods can be trained from scratch in our dataset and outperform the published variants. Finally, we introduce modifications to the PIPs point tracking method, greatly widening its temporal receptive field, which improves its performance on PointOdyssey as well as on two real-world benchmarks.

PointOdyssey: A Large-Scale Synthetic Dataset for Long-Term Point Tracking paper page: introduce PointOdyssey, a large-scale synthetic dataset, and data generation framework, for the training and evaluation of long-term fine-grained tracking algorithms. Our goal is to advance the state-of-the-art by placing emphasis on long videos with naturalistic motion. Toward the goal of naturalism, we animate deformable characters using real-world motion capture data, we build 3D scenes to match the motion capture environments, and we render camera viewpoints using trajectories mined via structure-from-motion on real videos. We create combinatorial diversity by randomizing character appearance, motion profiles, materials, lighting, 3D assets, and atmospheric effects. Our dataset currently includes 104 videos, averaging 2,000 frames long, with orders of magnitude more correspondence annotations than prior work. We show that existing methods can be trained from scratch in our dataset and outperform the published variants. Finally, we introduce modifications to the PIPs point tracking method, greatly widening its temporal receptive field, which improves its performance on PointOdyssey as well as on two real-world benchmarks.

AK

122,533 次观看 • 3 年前

GDN: The $KNDX Solution No more hardware bottlenecks Kondux GDN offers streamlined solutions to the problems users may face these days, such as limited access to state-of-the-art hardware & GPU power. We solve this by offloading heavy computational tasks to GDN while streaming 3D assets in real time to any device, regardless of local device capabilities. This means users can easily perform complex activities like high performance gaming, 3D VFX rendering & digital twin simulations for manufacturing without reliance on local GPU power & hardware. For creators, developers & enterprises, this eliminates massive roadblocks for their creative work flow. Instant collaboration through blockchain security In addition, our smart contracts enables sharing digital assets instant and secure, allowing for easy collaboration across multiple platforms. By utilizing our DNA technology, viewing a 3D asset doesn’t automatically grant ownership, and ensures creators 3D assets are protected from unauthorized use. Our kNFT tech brings life, personality & evolution to digital assets. Part of our mission is facilitating the shift of conversation from only traditional static NFTs, towards dynamic high-fidelity 3D assets with real world utility. Advanced interactivity & immersion in real time for all.

GDN: The $KNDX Solution No more hardware bottlenecks Kondux GDN offers streamlined solutions to the problems users may face these days, such as limited access to state-of-the-art hardware & GPU power. We solve this by offloading heavy computational tasks to GDN while streaming 3D assets in real time to any device, regardless of local device capabilities. This means users can easily perform complex activities like high performance gaming, 3D VFX rendering & digital twin simulations for manufacturing without reliance on local GPU power & hardware. For creators, developers & enterprises, this eliminates massive roadblocks for their creative work flow. Instant collaboration through blockchain security In addition, our smart contracts enables sharing digital assets instant and secure, allowing for easy collaboration across multiple platforms. By utilizing our DNA technology, viewing a 3D asset doesn’t automatically grant ownership, and ensures creators 3D assets are protected from unauthorized use. Our kNFT tech brings life, personality & evolution to digital assets. Part of our mission is facilitating the shift of conversation from only traditional static NFTs, towards dynamic high-fidelity 3D assets with real world utility. Advanced interactivity & immersion in real time for all.

Kondux

50,669 次观看 • 1 年前

Excited to share this demo from Over the Reality! Watch our Unitree Go2 robodog navigating our office while reconstructing the 3D space in real-time using the VGGT-based foundation vision model. This is a prime example of machine perception in action, turning raw RGB camera feeds into rich, detailed 3D maps! The robodog's RGB cam generates a dense, textured 3D reconstruction via VGGT from a few photograms, capturing nuances like object shapes and surfaces with impressive fidelity (main view). Compare that to the standard LiDAR system (top right), it's sparser, more point-cloud focused, lacking the visual richness. Vision models are closing the gap fast! What's powering this? VGGT, a cutting-edge foundation model for 3D perception, trained on datasets orders of magnitude smaller than our massive OVER 3D maps dataset. Imagine the leap when we apply OVER's scale to VGGT-like transformer based architectures, denser reconstructions, better generalization, revolutionary for robotics, machine perception & AR! Stay tuned for more breakthroughs at the intersection of AI, robotics, and DePIN. We're building the future of Physical AI and Spatial Computing at Over the Reality 🌐 What do you think, ready for robodogs in your world? Drop your thoughts! 🤖🌐

Excited to share this demo from Over the Reality! Watch our Unitree Go2 robodog navigating our office while reconstructing the 3D space in real-time using the VGGT-based foundation vision model. This is a prime example of machine perception in action, turning raw RGB camera feeds into rich, detailed 3D maps! The robodog's RGB cam generates a dense, textured 3D reconstruction via VGGT from a few photograms, capturing nuances like object shapes and surfaces with impressive fidelity (main view). Compare that to the standard LiDAR system (top right), it's sparser, more point-cloud focused, lacking the visual richness. Vision models are closing the gap fast! What's powering this? VGGT, a cutting-edge foundation model for 3D perception, trained on datasets orders of magnitude smaller than our massive OVER 3D maps dataset. Imagine the leap when we apply OVER's scale to VGGT-like transformer based architectures, denser reconstructions, better generalization, revolutionary for robotics, machine perception & AR! Stay tuned for more breakthroughs at the intersection of AI, robotics, and DePIN. We're building the future of Physical AI and Spatial Computing at Over the Reality 🌐 What do you think, ready for robodogs in your world? Drop your thoughts! 🤖🌐

Over the Reality 🌐

359,717 次观看 • 10 个月前

🚀 We are hiring! 𝗣𝗵𝗗 𝗦𝘁𝘂𝗱𝗲𝗻𝘁 𝗶𝗻 𝗖𝗔𝗥-𝗧 𝗖𝗲𝗹𝗹𝘂𝗹𝗮𝗿 𝗧𝗵𝗲𝗿𝗮𝗽𝘆 𝗮𝗻𝗱 𝗖𝗮𝗻𝗰𝗲𝗿 𝗡𝗲𝘂𝗿𝗼𝗶𝗺𝗺𝘂𝗻𝗼𝗹𝗼𝗴𝘆 We are looking for a passionate and motivated PhD student to join an exciting project focused on how nerve, immune and cancer cell interactions shape metastatic tumor behavior and resistance to cellular therapies, especially CAR-T cells. The project combines: • Whole-body tissue clearing • Light-sheet microscopy • wildDISCO-based immunolabeling • CAR-T and immune cell tracking in metastatic cancer • Spatial proteomics and 3D spatial biology • AI-based analysis of large-scale imaging datasets This is a great opportunity for someone with a background in cancer biology, immunology, neuroscience, biomedical sciences, biomedical engineering or a related field who wants to work at the interface of experimental biology, advanced imaging and AI. Apply here: Please share with excellent candidates who may be interested. #Hiring #PhDPosition #CARTcells #CellularTherapy #CancerResearch #Neuroimmunology #Immunotherapy #Metastasis #SpatialBiology #AIinBiology #Microscopy #LMUMunich #HelmholtzMunich #ErturkLab

🚀 We are hiring! 𝗣𝗵𝗗 𝗦𝘁𝘂𝗱𝗲𝗻𝘁 𝗶𝗻 𝗖𝗔𝗥-𝗧 𝗖𝗲𝗹𝗹𝘂𝗹𝗮𝗿 𝗧𝗵𝗲𝗿𝗮𝗽𝘆 𝗮𝗻𝗱 𝗖𝗮𝗻𝗰𝗲𝗿 𝗡𝗲𝘂𝗿𝗼𝗶𝗺𝗺𝘂𝗻𝗼𝗹𝗼𝗴𝘆 We are looking for a passionate and motivated PhD student to join an exciting project focused on how nerve, immune and cancer cell interactions shape metastatic tumor behavior and resistance to cellular therapies, especially CAR-T cells. The project combines: • Whole-body tissue clearing • Light-sheet microscopy • wildDISCO-based immunolabeling • CAR-T and immune cell tracking in metastatic cancer • Spatial proteomics and 3D spatial biology • AI-based analysis of large-scale imaging datasets This is a great opportunity for someone with a background in cancer biology, immunology, neuroscience, biomedical sciences, biomedical engineering or a related field who wants to work at the interface of experimental biology, advanced imaging and AI. Apply here: Please share with excellent candidates who may be interested. #Hiring #PhDPosition #CARTcells #CellularTherapy #CancerResearch #Neuroimmunology #Immunotherapy #Metastasis #SpatialBiology #AIinBiology #Microscopy #LMUMunich #HelmholtzMunich #ErturkLab

Ali Max Erturk

14,767 次观看 • 2 个月前

From 3D imaging to therapy Using our DISCO tissue clearing and whole organ 3D imaging platform, we revealed skull to meninges microchannels connecting calvarial bone marrow with the brain borders in mice (Cai...Ertürk, Nature Neuroscience,2018, We then extended this biology to humans, discovering distinct skull bone marrow programs and structural skull meninges connections in health and neurological disease (Kolabas...Ertürk, Cell, 2023). Now, a new study reports a first therapeutic application of this route: intracalvariosseous (calvarial) injection of drug loaded albumin nanoparticles to “hijack” calvarial immune cells, which then migrate via skull meninges microchannels to CNS lesions, bypassing the blood brain barrier and improving outcomes in stroke models, with encouraging clinical feasibility data (Gao et al., Cell, 2026) We are proud that DISCO enabled biology that is now turning into concrete therapeutic strategies for devastating neurological diseases. More to come. #DISCO #TissueClearing #Neuroimmunology #Stroke #Nanomedicine #DrugDelivery #BBB #CNS Refs (for linking in comments): Cai et al. Nat Neuroscience, Kolabas et al. Cell 2023, the video from Gao et al. Cell 2026 (online Jan 16, 2026).

From 3D imaging to therapy Using our DISCO tissue clearing and whole organ 3D imaging platform, we revealed skull to meninges microchannels connecting calvarial bone marrow with the brain borders in mice (Cai...Ertürk, Nature Neuroscience,2018, We then extended this biology to humans, discovering distinct skull bone marrow programs and structural skull meninges connections in health and neurological disease (Kolabas...Ertürk, Cell, 2023). Now, a new study reports a first therapeutic application of this route: intracalvariosseous (calvarial) injection of drug loaded albumin nanoparticles to “hijack” calvarial immune cells, which then migrate via skull meninges microchannels to CNS lesions, bypassing the blood brain barrier and improving outcomes in stroke models, with encouraging clinical feasibility data (Gao et al., Cell, 2026) We are proud that DISCO enabled biology that is now turning into concrete therapeutic strategies for devastating neurological diseases. More to come. #DISCO #TissueClearing #Neuroimmunology #Stroke #Nanomedicine #DrugDelivery #BBB #CNS Refs (for linking in comments): Cai et al. Nat Neuroscience, Kolabas et al. Cell 2023, the video from Gao et al. Cell 2026 (online Jan 16, 2026).

Ali Max Erturk

15,901 次观看 • 6 个月前