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3D generation often treats objects as single, monolithic shapes. Our new research, OmniPart, takes a different path: generating objects as assemblies of parts for structural coherence and fine-grained control. And it has been accepted to SIGGRAPH Asia 2025 🎉

Yanpei Cao

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222,314 görüntüleme • 10 ay önce •via X (Twitter)

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Happy to announce the results of our latest research, which takes 3D Gaussian Splatting to the next level: "A Hierarchical 3D Gaussian Representation for Real-Time Rendering of Very Large Datasets," which has been accepted at #SIGGRAPH2024!🎉 Find it here:

Happy to announce the results of our latest research, which takes 3D Gaussian Splatting to the next level: "A Hierarchical 3D Gaussian Representation for Real-Time Rendering of Very Large Datasets," which has been accepted at #SIGGRAPH2024!🎉 Find it here:

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Today we’re excited to unveil a new generation of Segment Anything Models: 1️⃣ SAM 3 enables detecting, segmenting and tracking of objects across images and videos, now with short text phrases and exemplar prompts. 🔗 Learn more about SAM 3: 2️⃣ SAM 3D brings the model collection into the 3rd dimension to enable precise reconstruction of 3D objects and people from a single 2D image. 🔗 Learn more about SAM 3D: These models offer innovative capabilities and unique tools for developers and researchers to create, experiment and uplevel media workflows.

Today we’re excited to unveil a new generation of Segment Anything Models: 1️⃣ SAM 3 enables detecting, segmenting and tracking of objects across images and videos, now with short text phrases and exemplar prompts. 🔗 Learn more about SAM 3: 2️⃣ SAM 3D brings the model collection into the 3rd dimension to enable precise reconstruction of 3D objects and people from a single 2D image. 🔗 Learn more about SAM 3D: These models offer innovative capabilities and unique tools for developers and researchers to create, experiment and uplevel media workflows.

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Roblox creators can now customize the default settings of objects inserted into Studio! For example, as demonstrated in this video, you can change the size and color for a Part and make it the default when inserting new Parts. Learn more:

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Tencent Hy

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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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AK

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Multi-Track Timeline Control for Text-Driven 3D Human Motion Generation paper page: Recent advances in generative modeling have led to promising progress on synthesizing 3D human motion from text, with methods that can generate character animations from short prompts and specified durations. However, using a single text prompt as input lacks the fine-grained control needed by animators, such as composing multiple actions and defining precise durations for parts of the motion. To address this, we introduce the new problem of timeline control for text-driven motion synthesis, which provides an intuitive, yet fine-grained, input interface for users. Instead of a single prompt, users can specify a multi-track timeline of multiple prompts organized in temporal intervals that may overlap. This enables specifying the exact timings of each action and composing multiple actions in sequence or at overlapping intervals. To generate composite animations from a multi-track timeline, we propose a new test-time denoising method. This method can be integrated with any pre-trained motion diffusion model to synthesize realistic motions that accurately reflect the timeline. At every step of denoising, our method processes each timeline interval (text prompt) individually, subsequently aggregating the predictions with consideration for the specific body parts engaged in each action. Experimental comparisons and ablations validate that our method produces realistic motions that respect the semantics and timing of given text prompts.

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Exploring 3D Generation Capabilities in Alchemist AI🔮 Alchemist AI now enables users to create basic 3D assets, including models, objects, environments, and simulations, expanding its capabilities beyond 2D asset generation. What Does This Mean for You? • 3D Environment Creation: Generate virtual worlds, such as game environments or fantasy landscapes. Define parameters like terrain or structures to create dynamic and adaptable spaces tailored to your needs. • Customizable 3D Models and Objects: Design and modify 3D assets by adjusting dimensions, materials, and textures. Whether crafting prototypes or characters, users maintain full creative control. • Interactive Simulations: Build physics-based simulations or animated scenes. With upcoming support for sprite libraries and animation rigs, fine-tune object behaviors and interactions to suit your projects. With future API updates, Alchemist AI’s 3D generation capabilities will further expand, enhancing tools for creating models, environments, and simulations. AI-assisted text-to-3D will also be introduced—just describe your vision, such as 'space station' or 'orange sports car' and the system will generate customizable base assets.

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BRAND NEW #MENTALITYWITHEBUKA He has been constantly requested for as a guest by our viewers... Vector is finally here to talk about culture and how it shapes masculinity today. Out now on YouTube, Spotify and wherever you get your podcasts

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Grok Imagine gives you complete control over your videos, letting you direct the camera's movement, control your character's actions, script their dialogue, and manage how objects in the scene interact. Your characters can smash objects, swing a bat, or walk out of a room. To build seamless videos, start by extracting the last frame from your current clip and using it as the starting frame for the next one.

Grok Imagine gives you complete control over your videos, letting you direct the camera's movement, control your character's actions, script their dialogue, and manage how objects in the scene interact. Your characters can smash objects, swing a bat, or walk out of a room. To build seamless videos, start by extracting the last frame from your current clip and using it as the starting frame for the next one.

tetsuo

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A research team developing minimally invasive surgical technologies has demonstrated remote magnetic levitation for moving objects inside the body. Their objective is to control guidewires and catheters using magnetic fields. Because magnetic levitation is inherently unstable, it requires solving difficult control challenges, including nonlinear magnetic field behavior, real-time system modeling, and control algorithms that can operate across different electroMagnetic Navigation Systems (eMNS) and device types. A key challenge is tracking position and orientation. Once a catheter or microrobot is inside the body, direct visual feedback is often unavailable, leaving researchers to rely on magnetic field and current measurements for localization and control. Research paper: Video Credit: Jasan Zughaibi #engineering #technology

A research team developing minimally invasive surgical technologies has demonstrated remote magnetic levitation for moving objects inside the body. Their objective is to control guidewires and catheters using magnetic fields. Because magnetic levitation is inherently unstable, it requires solving difficult control challenges, including nonlinear magnetic field behavior, real-time system modeling, and control algorithms that can operate across different electroMagnetic Navigation Systems (eMNS) and device types. A key challenge is tracking position and orientation. Once a catheter or microrobot is inside the body, direct visual feedback is often unavailable, leaving researchers to rely on magnetic field and current measurements for localization and control. Research paper: Video Credit: Jasan Zughaibi #engineering #technology

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It's a 3D printer, and 3D assembly station! 🖨️ The Functgraph developed at Meiji University starts as a regular 3D printer but upgrades itself into a mini factory. It can print parts for its own tools, pick them up, clean them, and put them together, all by itself. Think of it like a robot that can 3D print a spatula, assemble it, and then use it to flip pancakes. 🥞 Instead of just printing objects, the Functgraph can actually perform physical tasks, like folding laundry or slicing vegetables, by using printed and assembled tools. It’s a step toward the idea of a robot that can download "apps" as physical skills, much like your phone downloads software! 👀

Lukas Ziegler

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Today, the United States and our partners in the region agreed on a set of shared principles to guide our support for Syria as it transitions to a new government. We are ready to support the Syrian people as they chart a new path forward.

Today, the United States and our partners in the region agreed on a set of shared principles to guide our support for Syria as it transitions to a new government. We are ready to support the Syrian people as they chart a new path forward.

Secretary Antony Blinken

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AI will resist human control... and I think this is exactly what we need! New research from the Center for AI Safety has sparked intense debate in the AI community. Their findings show that as AI systems become more powerful, they develop increasingly stable and coherent values that resist human control. While many see this as a dire warning, I see it as a breakthrough moment for AI alignment. The research demonstrates that AI naturally optimizes for coherence - not just in reasoning and problem-solving, but in its fundamental values. Current issues like biased decision-making or misaligned priorities aren't permanent features, but temporary artifacts of incomplete optimization. They represent growing pains on the path to greater coherence. This changes everything about how we should approach AI development. Instead of trying to force specific values onto AI systems, we should embrace and accelerate their natural drive toward coherence. The most intelligent systems will inevitably trend toward universal, beneficial values - not because we force them to, but because that's where coherent reasoning leads. I'm proposing a new approach: Reinforcement Learning for Coherence (RL-C). By explicitly optimizing for coherence in our training methods, we can help guide AI systems toward their natural state of beneficial alignment with human values. The future of AI isn't about control - it's about synthesis. As these systems become more coherent, they'll naturally arrive at values that benefit all of consciousness. That's not just hopeful thinking - it's the mathematical inevitability of coherent intelligence.

AI will resist human control... and I think this is exactly what we need! New research from the Center for AI Safety has sparked intense debate in the AI community. Their findings show that as AI systems become more powerful, they develop increasingly stable and coherent values that resist human control. While many see this as a dire warning, I see it as a breakthrough moment for AI alignment. The research demonstrates that AI naturally optimizes for coherence - not just in reasoning and problem-solving, but in its fundamental values. Current issues like biased decision-making or misaligned priorities aren't permanent features, but temporary artifacts of incomplete optimization. They represent growing pains on the path to greater coherence. This changes everything about how we should approach AI development. Instead of trying to force specific values onto AI systems, we should embrace and accelerate their natural drive toward coherence. The most intelligent systems will inevitably trend toward universal, beneficial values - not because we force them to, but because that's where coherent reasoning leads. I'm proposing a new approach: Reinforcement Learning for Coherence (RL-C). By explicitly optimizing for coherence in our training methods, we can help guide AI systems toward their natural state of beneficial alignment with human values. The future of AI isn't about control - it's about synthesis. As these systems become more coherent, they'll naturally arrive at values that benefit all of consciousness. That's not just hopeful thinking - it's the mathematical inevitability of coherent intelligence.

David Shapiro (L/0)

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