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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

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66,918 görüntüleme • 1 yıl önce •via X (Twitter)

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Sherwin Bahmani profil fotoğrafı

Sherwin Bahmani1 yıl önce

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.

Sherwin Bahmani profil fotoğrafı

Sherwin Bahmani1 yıl önce

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.

Sherwin Bahmani profil fotoğrafı

Sherwin Bahmani1 yıl önce

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

Sherwin Bahmani profil fotoğrafı

Sherwin Bahmani1 yıl önce

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

Sherwin Bahmani profil fotoğrafı

Sherwin Bahmani1 yıl önce

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

Sherwin Bahmani profil fotoğrafı

Sherwin Bahmani1 yıl önce

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

Sherwin Bahmani profil fotoğrafı

Sherwin Bahmani1 yıl önce

@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

Sherwin Bahmani profil fotoğrafı

Sherwin Bahmani1 yıl önce

@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.

Matthias Niessner profil fotoğrafı

Matthias Niessner1 yıl önce

Amazing work!

Sherwin Bahmani profil fotoğrafı

Sherwin Bahmani1 yıl önce

Thanks Matthias 🍻

Kai He profil fotoğrafı

Kai He1 yıl önce

Strong 👍👍🥳

Sherwin Bahmani profil fotoğrafı

Sherwin Bahmani1 yıl önce

Not as strong as you 😌

Eduard Zamfir profil fotoğrafı

Eduard Zamfir1 yıl önce

Uff 😮‍💨 sky is the limit! 🚀

Sherwin Bahmani profil fotoğrafı

Sherwin Bahmani1 yıl önce

Danke du Spruecheklopfer 🤪

Oliver Hahn profil fotoğrafı

Oliver Hahn1 yıl önce

Machine! Congrats 🚀

Sherwin Bahmani profil fotoğrafı

Sherwin Bahmani1 yıl önce

Danke Bro 😎

Lily Goli profil fotoğrafı

Lily Goli1 yıl önce

looks amazing!! 🎉

Sherwin Bahmani profil fotoğrafı

Sherwin Bahmani1 yıl önce

Thanks Lily 😊

Stephen J. Caggiano profil fotoğrafı

Stephen J. Caggiano1 yıl önce

🔥❤️👏🏼

Ziya Erkoç profil fotoğrafı

Ziya Erkoç1 yıl önce

Congrats, Sherwin!🎉🎉

Sherwin Bahmani profil fotoğrafı

Sherwin Bahmani1 yıl önce

Thanks Ziya 🫡

Tuan Ngo profil fotoğrafı

Tuan Ngo1 yıl önce

Amazing!!! Congrats Sherwin

Sherwin Bahmani profil fotoğrafı

Sherwin Bahmani1 yıl önce

Thanks big guy 🙏

Ison Ward profil fotoğrafı

Ison Ward1 yıl önce

Cool！That looks like a good time

Sai Tedla profil fotoğrafı

Sai Tedla1 yıl önce

This is insane.

Phil Franco profil fotoğrafı

Phil Franco1 yıl önce

that looks cool!

Yuyang Zhao profil fotoğrafı

Yuyang Zhao1 yıl önce

Amazing work! Congrats, Sherwin!

Sherwin Bahmani profil fotoğrafı

Sherwin Bahmani1 yıl önce

Thanks Yuyang 🤪

Hadi Alzayer profil fotoğrafı

Hadi Alzayer1 yıl önce

Very cool work! Congrats on the release

Sherwin Bahmani profil fotoğrafı

Sherwin Bahmani1 yıl önce

Thanks Hadi 😊

:3 profil fotoğrafı

:31 yıl önce

This is over 20 years ago:

Felix Taubner profil fotoğrafı

Felix Taubner1 yıl önce

The 4D 🐐 🤩

Sherwin Bahmani profil fotoğrafı

Sherwin Bahmani1 yıl önce

No, you are the 4D 🐐😮‍💨

Mohamad Shahbazi profil fotoğrafı

Mohamad Shahbazi1 yıl önce

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

Sherwin Bahmani profil fotoğrafı

Sherwin Bahmani1 yıl önce

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!

Ethan Weber profil fotoğrafı

Ethan Weber1 yıl önce

Congrats! I really like the ideas here. 🙂

Sherwin Bahmani profil fotoğrafı

Sherwin Bahmani1 yıl önce

Thanks Ethan 😊

Andrew Guo profil fotoğrafı

Andrew Guo7 ay önce

wow! this is cool

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Tencent Hy

198,391 görüntüleme • 1 yıl önce

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 görüntüleme • 3 yıl önce

🚀 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 görüntüleme • 2 yıl önce