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📢GaussianGPT: autoregressive 3D Gaussian scene generation. We introduce a GPT-style model that directly generates 3D Gaussian scenes, token by token, in a series of small, discrete decision steps. Generation, completion, and large-scale outpainting in a single pipeline. Unlike diffusion-based approaches, GaussianGPT explicitly models the scene distribution at every step,... show more

Matthias Niessner

49,718 subscribers

152,626 次观看 • 6 个月前 •via X (Twitter)

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31 条评论

Gregor 的头像

Gregor6 个月前

I'm not sure "token by token" is the right framing here. Gaussians aren't really discrete units the way words are, so forcing that paradigm seems like it's fighting the representation more than leveraging it.

Clément Jambon 的头像

Clément Jambon6 个月前

Very cool work! We followed a quite similar pipeline a couple of year ago to generate scenes from a single-exemplar: In our case, latent features were DINO and Gaussians were decoded with a patch-based synthesis step (inspired by PatchMatch).

Andreas Klinger 🦾 的头像

Andreas Klinger 🦾6 个月前

this is really interesting! does this mean it builds a scene "meter by meter" (tokens as splats done over distance)? if yes there is tons of usecases for this - eg worldmodels for robotics, interactive games, VR, or simply generation of scenes, etc etc all combined with really fast rendering longer term end-state here could be, a model that does "chain of thought" directly in latent space and allows a worldmodel to reason not in pixels but full environments at absurd speed lmk if understanding this is BS and also kudos @nicolasvluetzow @barbara_roessle @katha_schmid

Matthias Niessner 的头像

Matthias Niessner6 个月前

Yes, pretty much, although tokens are smaller granularity than meters. We also see the same use cases :)

Andreas Klinger 🦾 的头像

Andreas Klinger 🦾6 个月前

yes - was more meant as figure of speech obvious genuinely - this is the most exciting thing i have seen a long time in that space true 3d worldmodels - not 2d hacks

Evan Bacon 🥓 的头像

Evan Bacon 🥓6 个月前

Incredible

Florian Hoenig 的头像

Florian Hoenig6 个月前

Pretty coooool :-) You think this approach could work with SDF primitives too?

Nabil Ess 的头像

Nabil Ess6 个月前

This is sick! 🔥No input photos, no COLMAP, no Meshroom, no classic Structure-from-Motion at generation time.

Mathieu Tuli 的头像

Mathieu Tuli6 个月前

very cool!

kieran.base.eth 的头像

kieran.base.eth6 个月前

that's sick I was trying to do AR a while back i feel like this would bang

Micha 的头像

Micha6 个月前

@nicolasvluetzow Is this open source?

Bika.ai 的头像

Bika.ai6 个月前

We are moving from capturing reality to predicting it. The data moat just got deeper.

Ajinkya Gorad 的头像

Ajinkya Gorad6 个月前

how about object by object, LLM recalls the scene that gets generated in its space, whether in gauss splats or text clearly sequential representation here is shown, much like image generation in chatgpt, is it volumetric generation using LLM, but with using gaussian?

Cris Lenta 的头像

Cris Lenta6 个月前

this is awesome!

Victor Kyriazakos 的头像

Victor Kyriazakos6 个月前

Awesome work! Can’t wait to integrate this into our StudioTwin platform and bring it to the hands of UE5 filmmakers. What’s the performance like for a full scene generation?

Manish 的头像

Manish6 个月前

dream come true type shi

Max Fowler 的头像

Max Fowler6 个月前

HOly Ish 😮

earendil 的头像

earendil6 个月前

@janusch_patas interesting

David Aerne 的头像

David Aerne6 个月前

impressive!

INT 16H 的头像

INT 16H6 个月前

Will you release all trained models? Would tremendously help with adjacent research

antoine 的头像

antoine5 个月前

so we're basically teaching GPT to think in 3D now? next thing you know it'll be generating entire metaverses token by token while i'm still trying to get my gaussian splats to not look like abstract art gone wrong

Conor 的头像

Conor6 个月前

love it

Rey Neill 的头像

Rey Neill6 个月前

Cool

mr.tipton 的头像

mr.tipton6 个月前

Are you saying this generates a point cloud that doesn’t break at certain camera angles?

Krish Shah 的头像

Krish Shah6 个月前

does this create splats from images, text or as of now just edit them?

Will {AI} 的头像

Will {AI}6 个月前

When will be codea? If there will be ability to choose image gen models?

Alec Yiu 的头像

Alec Yiu6 个月前

Very cool!

ioannis karavas 的头像

ioannis karavas6 个月前

cc @SirWrender

dailypapers.io 的头像

dailypapers.io6 个月前

🚀

Nyck Maftum 的头像

Nyck Maftum6 个月前

oMG

Zev Rekhter 的头像

Zev Rekhter6 个月前

any credible application of this could 100x compute demand

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AK

62,768 次观看 • 3 年前

LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models paper page: github: Recent advancements in text-to-image generation with diffusion models have yielded remarkable results synthesizing highly realistic and diverse images. However, these models still encounter difficulties when generating images from prompts that demand spatial or common sense reasoning. We propose to equip diffusion models with enhanced reasoning capabilities by using off-the-shelf pretrained large language models (LLMs) in a novel two-stage generation process. First, we adapt an LLM to be a text-guided layout generator through in-context learning. When provided with an image prompt, an LLM outputs a scene layout in the form of bounding boxes along with corresponding individual descriptions. Second, we steer a diffusion model with a novel controller to generate images conditioned on the layout. Both stages utilize frozen pretrained models without any LLM or diffusion model parameter optimization. We validate the superiority of our design by demonstrating its ability to outperform the base diffusion model in accurately generating images according to prompts that necessitate both language and spatial reasoning. Additionally, our method naturally allows dialog-based scene specification and is able to handle prompts in a language that is not well-supported by the underlying diffusion model.

LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models paper page: github: Recent advancements in text-to-image generation with diffusion models have yielded remarkable results synthesizing highly realistic and diverse images. However, these models still encounter difficulties when generating images from prompts that demand spatial or common sense reasoning. We propose to equip diffusion models with enhanced reasoning capabilities by using off-the-shelf pretrained large language models (LLMs) in a novel two-stage generation process. First, we adapt an LLM to be a text-guided layout generator through in-context learning. When provided with an image prompt, an LLM outputs a scene layout in the form of bounding boxes along with corresponding individual descriptions. Second, we steer a diffusion model with a novel controller to generate images conditioned on the layout. Both stages utilize frozen pretrained models without any LLM or diffusion model parameter optimization. We validate the superiority of our design by demonstrating its ability to outperform the base diffusion model in accurately generating images according to prompts that necessitate both language and spatial reasoning. Additionally, our method naturally allows dialog-based scene specification and is able to handle prompts in a language that is not well-supported by the underlying diffusion model.

AK

83,681 次观看 • 3 年前

Introducing Teleport 2.0, a next-generation release for our 3D capture solution that sets a new benchmark in cloud-based Gaussian Splatting. Teleport 2.0 is all about lowering the barrier of entry to create and share lifelike 3D captures. New capabilities include: ▪️ Upgraded 3D reconstruction engine: Preserves real-world detail, even in large-scale scenes that can be captured in minutes and automatically processed without manual steps. ▪️Support for any camera: Capture with phones, drones, professional cameras, or other imaging devices. ▪️ Tools for creating engaging immersive experiences: Build virtual tours, or link multiple scans together to build massive explorable digital twins. Read more: #3D #DigitalTwin #GaussianSplatting

Introducing Teleport 2.0, a next-generation release for our 3D capture solution that sets a new benchmark in cloud-based Gaussian Splatting. Teleport 2.0 is all about lowering the barrier of entry to create and share lifelike 3D captures. New capabilities include: ▪️ Upgraded 3D reconstruction engine: Preserves real-world detail, even in large-scale scenes that can be captured in minutes and automatically processed without manual steps. ▪️Support for any camera: Capture with phones, drones, professional cameras, or other imaging devices. ▪️ Tools for creating engaging immersive experiences: Build virtual tours, or link multiple scans together to build massive explorable digital twins. Read more: #3D #DigitalTwin #GaussianSplatting

Varjo

21,245 次观看 • 1 年前

Nvidia announces GAvatar: Animatable 3D Gaussian Avatars with Implicit Mesh Learning paper page: Gaussian splatting has emerged as a powerful 3D representation that harnesses the advantages of both explicit (mesh) and implicit (NeRF) 3D representations. In this paper, we seek to leverage Gaussian splatting to generate realistic animatable avatars from textual descriptions, addressing the limitations (e.g., flexibility and efficiency) imposed by mesh or NeRF-based representations. However, a naive application of Gaussian splatting cannot generate high-quality animatable avatars and suffers from learning instability; it also cannot capture fine avatar geometries and often leads to degenerate body parts. To tackle these problems, we first propose a primitive-based 3D Gaussian representation where Gaussians are defined inside pose-driven primitives to facilitate animation. Second, to stabilize and amortize the learning of millions of Gaussians, we propose to use neural implicit fields to predict the Gaussian attributes (e.g., colors). Finally, to capture fine avatar geometries and extract detailed meshes, we propose a novel SDF-based implicit mesh learning approach for 3D Gaussians that regularizes the underlying geometries and extracts highly detailed textured meshes. Our proposed method, GAvatar, enables the large-scale generation of diverse animatable avatars using only text prompts. GAvatar significantly surpasses existing methods in terms of both appearance and geometry quality, and achieves extremely fast rendering (100 fps) at 1K resolution.

AK

141,058 次观看 • 2 年前