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Video to 3D Memory (MiDAS v3.1 + #glslViewer +

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patricio gonzalez vivo

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137,365 просмотров • 3 лет назад •via X (Twitter)

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Minecraft 3D generation now at the highest quality ever, built using fal ⛏️ Text → Image (nano-banana-pro) → 3D mesh (Hunyuan 3D v3.1) → Voxelize → Texture mapping → Minecraft structure!
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Minecraft 3D generation now at the highest quality ever, built using fal ⛏️ Text → Image (nano-banana-pro) → 3D mesh (Hunyuan 3D v3.1) → Voxelize → Texture mapping → Minecraft structure!

Blendi

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Turn any webpage into a museum! 🏛️ Made this Chrome extension that uses Hunyuan 3D v3.1 on fal to turn any image into a 3D model, and three.js for the interactive viewer
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Turn any webpage into a museum! 🏛️ Made this Chrome extension that uses Hunyuan 3D v3.1 on fal to turn any image into a 3D model, and three.js for the interactive viewer

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how i feel listening to midas touch
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how i feel listening to midas touch

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Welcome to #Chelsea, Alex! 💙 / Midas Sports Management
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Welcome to #Chelsea, Alex! 💙 / Midas Sports Management

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They’re def gonna be late to the wedding #Fortnite #Midas #FortniteArt
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Vincenciel

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The context size of video world models is only a few frames. Like a human with severe memory loss! We design a long-term memory for world models based on explicit 3D representations inspired by the human mind. This enables long-term consistency. 1/3
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The context size of video world models is only a few frames. Like a human with severe memory loss! We design a long-term memory for world models based on explicit 3D representations inspired by the human mind. This enables long-term consistency. 1/3

Gordon Wetzstein

35,095 просмотров • 1 год назад

MIT researchers taught a robot dog to perceive a 3D world using Neural Volumetric Memory (NVM). This technique lets the bot climb stairs, step over gaps & run autonomously w/a single neural network. Paper: Video: w/UC San Diego
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MIT researchers taught a robot dog to perceive a 3D world using Neural Volumetric Memory (NVM). This technique lets the bot climb stairs, step over gaps & run autonomously w/a single neural network. Paper: Video: w/UC San Diego

MIT CSAIL

507,641 просмотров • 3 лет назад

LGTM from Apple: 4K feed-forward 3D Gaussian Splatting. instant 4K 3D scenes without massive GPUs.. - predicts a few lightweight 3D shapes, wraps them in ultra-high-res 2D textures. - low Memory usage You take two normal photos of a room. Instantly walk around it in flawless 3D.
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LGTM from Apple: 4K feed-forward 3D Gaussian Splatting. instant 4K 3D scenes without massive GPUs.. - predicts a few lightweight 3D shapes, wraps them in ultra-high-res 2D textures. - low Memory usage You take two normal photos of a room. Instantly walk around it in flawless 3D.

Wildminder

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Yu et al., "MosaicMem: Hybrid Spatial Memory for Controllable Video World Models" A patch-based spatial memory that you raster into views + glues to make things work.
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Yu et al., "MosaicMem: Hybrid Spatial Memory for Controllable Video World Models" A patch-based spatial memory that you raster into views + glues to make things work.

Kwang Moo Yi

11,170 просмотров • 4 месяцев назад

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.
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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.

MrNeRF

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A lot of work over the weekend: LYGIA: cmake/python bundle script for C++ VERA v1.1.0: - support for gsplats - LYGIA bundle to stream line default shaders - better camera interactions GlslViewer v3.5.1: support for gsplats and native lygia support
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A lot of work over the weekend: LYGIA: cmake/python bundle script for C++ VERA v1.1.0: - support for gsplats - LYGIA bundle to stream line default shaders - better camera interactions GlslViewer v3.5.1: support for gsplats and native lygia support

patricio gonzalez vivo

15,097 просмотров • 6 месяцев назад

I don’t have the BRAM for a depth buffer so it’s time to get the memory controller up and running for this fjord torus. 100% FPGA logic 3D pipeline, no CPU.
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I don’t have the BRAM for a depth buffer so it’s time to get the memory controller up and running for this fjord torus. 100% FPGA logic 3D pipeline, no CPU.

Ian Hanschen

29,249 просмотров • 6 месяцев назад

3D rendering will change in a big way. Sora video to video and Dreams on my PS5🔥 #art #ai
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3D rendering will change in a big way. Sora video to video and Dreams on my PS5🔥 #art #ai

Martin Nebelong

18,527 просмотров • 1 год назад

some 3D animations i did for the Pink Pantheress Close To You music video >:)
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some 3D animations i did for the Pink Pantheress Close To You music video >:)

‎red

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Peter Doocy appears to be having memory issues while attacking Joe Biden's memory

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OUTLAW MIDAS' EMOTE FINALLY DROPPED 🔥 GET IT HERE: Use Code "Shiina" to support me, thank you!! ❤️ #EpicPartner
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OUTLAW MIDAS' EMOTE FINALLY DROPPED 🔥 GET IT HERE: Use Code "Shiina" to support me, thank you!! ❤️ #EpicPartner

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