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📢 Lyra: Generative 3D Scene Reconstruction via Video Diffusion Model Self-Distillation Got only one or a few images and wondering if recovering the 3D environment is a reconstruction or generation problem? Why not do it with a generative reconstruction model! We show that a camera-conditioned video diffusion model can... show more

Sherwin Bahmani

1,393 subscribers

66,918 Aufrufe • vor 1 Jahr •via X (Twitter)

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Profilbild von Sherwin Bahmani

Sherwin Bahmanivor 1 Jahr

Previous work (left) trains image-to-3D models using real-world datasets with limited diversity. In contrast, we propose a self-distillation framework (right) for generative 3D scene reconstruction. Specifically, a pre-trained camera-controlled video diffusion model (GEN3C) with its RGB decoder output (teacher) supervises the rendering of the 3DGS decoder (student). Using a video model pre-trained on large-scale real-world 2D video data allows us to provide diverse and potentially infinite multi-view supervision.

Profilbild von Sherwin Bahmani

Sherwin Bahmanivor 1 Jahr

Our model generates 3DGS scenes from a single input image in a feed-forward manner generalizing across a wide range of scenarios., without the need for per-scene optimization.

Profilbild von Sherwin Bahmani

Sherwin Bahmanivor 1 Jahr

Moreover, our framework easily extends to feed-forward 4D scene generation from a single input video.

Profilbild von Sherwin Bahmani

Sherwin Bahmanivor 1 Jahr

Our approach seamlessly generalizes to full 3D scene generation from a single real image, without ever being trained on real-world data.

Profilbild von Sherwin Bahmani

Sherwin Bahmanivor 1 Jahr

The generated 3D Gaussians can then be exported into into NVIDIA Isaac Sim based on 3DGUT to simulate humanoid robots in generated environments.

Profilbild von Sherwin Bahmani

Sherwin Bahmanivor 1 Jahr

Joint work with @TianchangS @jiawei6_ren @huangjh_hjh Yifeng Jiang @Haithem_Turki @taiyasaki @davelindell @ZGojcic @FidlerSanja @HuanLing6 @JunGao33210520 @xuanchi13

Profilbild von Sherwin Bahmani

Sherwin Bahmanivor 1 Jahr

@jiawei6_ren @huangjh_hjh @Haithem_Turki @taiyasaki @DaveLindell @ZGojcic @FidlerSanja @HuanLing6 @JunGao33210520 @xuanchi13 Check out this very related work Bolt3D ( at ICCV next month by @StanSzymanowicz @jasonyzhang2 @_pratul_ @RuiqiGao @ArthurBrussee @holynski_ @rmbrualla @jon_barron @philipphenzler

Profilbild von Sherwin Bahmani

Sherwin Bahmanivor 1 Jahr

@jiawei6_ren @huangjh_hjh @Haithem_Turki @taiyasaki @DaveLindell @ZGojcic @FidlerSanja @HuanLing6 @JunGao33210520 @xuanchi13 @StanSzymanowicz @jasonyzhang2 @_pratul_ @RuiqiGao @holynski_ @rmbrualla @jon_barron @philipphenzler Special thanks to @robrombach @andi_blatt and the rest of the team at @bfl_ml for open-sourcing FLUX ( We use FLUX to generate high-quality input images and lift them into 3D.

Profilbild von Matthias Niessner

Matthias Niessnervor 1 Jahr

Amazing work!

Profilbild von Sherwin Bahmani

Sherwin Bahmanivor 1 Jahr

Thanks Matthias 🍻

Profilbild von Kai He

Kai Hevor 1 Jahr

Strong 👍👍🥳

Profilbild von Sherwin Bahmani

Sherwin Bahmanivor 1 Jahr

Not as strong as you 😌

Profilbild von Eduard Zamfir

Eduard Zamfirvor 1 Jahr

Uff 😮‍💨 sky is the limit! 🚀

Profilbild von Sherwin Bahmani

Sherwin Bahmanivor 1 Jahr

Danke du Spruecheklopfer 🤪

Profilbild von Oliver Hahn

Oliver Hahnvor 1 Jahr

Machine! Congrats 🚀

Profilbild von Sherwin Bahmani

Sherwin Bahmanivor 1 Jahr

Danke Bro 😎

Profilbild von Lily Goli

Lily Golivor 1 Jahr

looks amazing!! 🎉

Profilbild von Sherwin Bahmani

Sherwin Bahmanivor 1 Jahr

Thanks Lily 😊

Profilbild von Stephen J. Caggiano

Stephen J. Caggianovor 1 Jahr

🔥❤️👏🏼

Profilbild von Ziya Erkoç

Ziya Erkoçvor 1 Jahr

Congrats, Sherwin!🎉🎉

Profilbild von Sherwin Bahmani

Sherwin Bahmanivor 1 Jahr

Thanks Ziya 🫡

Profilbild von Tuan Ngo

Tuan Ngovor 1 Jahr

Amazing!!! Congrats Sherwin

Profilbild von Sherwin Bahmani

Sherwin Bahmanivor 1 Jahr

Thanks big guy 🙏

Profilbild von Ison Ward

Ison Wardvor 1 Jahr

Cool！That looks like a good time

Profilbild von Sai Tedla

Sai Tedlavor 1 Jahr

This is insane.

Profilbild von Phil Franco

Phil Francovor 1 Jahr

that looks cool!

Profilbild von Yuyang Zhao

Yuyang Zhaovor 1 Jahr

Amazing work! Congrats, Sherwin!

Profilbild von Sherwin Bahmani

Sherwin Bahmanivor 1 Jahr

Thanks Yuyang 🤪

Profilbild von Hadi Alzayer

Hadi Alzayervor 1 Jahr

Very cool work! Congrats on the release

Profilbild von Sherwin Bahmani

Sherwin Bahmanivor 1 Jahr

Thanks Hadi 😊

Profilbild von :3

:3vor 1 Jahr

This is over 20 years ago:

Profilbild von Felix Taubner

Felix Taubnervor 1 Jahr

The 4D 🐐 🤩

Profilbild von Sherwin Bahmani

Sherwin Bahmanivor 1 Jahr

No, you are the 4D 🐐😮‍💨

Profilbild von Mohamad Shahbazi

Mohamad Shahbazivor 1 Jahr

Really cool work and amazing results Sherwin! :) Was wondering, how sensitive the 3DGS decoder is to the multivew inconsistencies?

Profilbild von Sherwin Bahmani

Sherwin Bahmanivor 1 Jahr

Thanks! We learn to smoothen inconsistencies with LPIPS loss. Obviously, a more consistent diffusion model will make it easier to get consistent output. The current viewpoint change is mainly limited because the diffusion output gets more and more inconsistent far away from the input view. Hope to improve that!

Profilbild von Ethan Weber

Ethan Webervor 1 Jahr

Congrats! I really like the ideas here. 🙂

Profilbild von Sherwin Bahmani

Sherwin Bahmanivor 1 Jahr

Thanks Ethan 😊

Profilbild von Andrew Guo

Andrew Guovor 7 Monaten

wow! this is cool

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198,391 Aufrufe • vor 1 Jahr

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 Aufrufe • vor 3 Jahren

🚀 Our NeurIPS '24 work, Large Spatial Model (LSM), is here! LSM performs semantic 3D reconstruction in just 0.1s, processing unposed data via feed-forward 3D reconstruction. 👉It leverages large-scale 3D datasets with minimal annotations, defining a 3D latent space. We are continuously exploring how this explicit 3D representation can further enhance reasoning and robotic learning. 🔗 Try our online Gradio demo with your own data at #NeurIPS2024 #3DReconstruction

🚀 Our NeurIPS '24 work, Large Spatial Model (LSM), is here! LSM performs semantic 3D reconstruction in just 0.1s, processing unposed data via feed-forward 3D reconstruction. 👉It leverages large-scale 3D datasets with minimal annotations, defining a 3D latent space. We are continuously exploring how this explicit 3D representation can further enhance reasoning and robotic learning. 🔗 Try our online Gradio demo with your own data at #NeurIPS2024 #3DReconstruction

Zhiwen(Aaron) Fan

44,051 Aufrufe • vor 2 Jahren