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🍺 LagerNVS (CVPR 2026) 🍺 LagerNVS is a generalizable, feed-forward, real-time Novel View Synthesis network which - performs rendering in real time, - generalizes to in-the-wild data, - works with and without known source cameras, - sets a new state-of-the-art among deterministic methods, - can be paired with a... diffusion decoder for generative extrapolation. LagerNVS shows that 3D biases are useful for Novel View Synthesis but explicit 3D representations are not required to achieve them. We use 3D biases in (1) architecture design and (2) pre-training: (1) In NVS with explicit 3D representations (3DGS, NeRF) reconstruction is typically difficult and slow, but rendering is much faster and simpler. We mimic this process in the network design: we use a large (1B params) encoder and a small, lightweight decoder (ViT-B). This allows increasing the network capacity while still achieving real-time rendering. (2) The encoder, initialized from VGGT, was pre-trained with 3D reconstruction objectives, making the initial features 3D aware. Both substantially improve performance. Project page: Code: Paper: Models: Work done with Jianyuan Minghao Chen Christian Rupprecht and Andrea Vedaldishow more

Stan Szymanowicz

1,165 subscribers

33,180 views • 7 months ago •via X (Twitter)

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15 Comments

Haian Jin7 months ago

Congrats, Stan! Really nice paper with impressive results! One thing I wanted to mention is that LargerNVS has a larger model size than LVSM here. It uses roughly a 1B-parameter encoder and a ~100M decoder, whereas LVSM has about ~100M + 100M parameters for the encoder-decoder variant, and ~200M total for the decoder-only variant. My guess is that the decoder-only variant would still perform better if scaled to a similar model size, since it's simply more end-to-end. That said, I completely agree that the encoder-decoder design is more suitable for practical applications such as real-time rendering.

Stan Szymanowicz7 months ago

Thanks Haian - it's a great point, our network is much bigger. We decided to keep the decoder size as the control variable, because it determines the rendering speed. The point is exactly that by using the encoder-decoder design we can 'cheat', increasing the size of the network while keeping rendering speed the same.

Stan Szymanowicz7 months ago

But yes your point is 100% valid - the optimal capacity allocation probably depends on applications you have in mind. I personally like interactive applications, so we wanted to keep real-time rendering constraint, but if you relaxed that then decoder-only could be the way.

Stan Szymanowicz7 months ago

And I just wanted to say that LVSM was a true inspiration for me (and a challenge to my research views :) ) - great work!

Haian Jin7 months ago

Thanks for your kind words, Stan! I completely agree that using an encoder–decoder design and scaling up the encoder size is a very good design. This is also what the great pioneering SRT work aimed to do, though its decoder may have been too small, and pure cross-attention might not be the most ideal design for NVS. Congrats to LargeNVS for making this direction work so much better!

Anagh Malik7 months ago

Congrats Stan!!! Looks amazing 🤩

Stan Szymanowicz7 months ago

Thanks Anagh!!

Abdullah Hamdi7 months ago

Awsome !

Yushi LAN7 months ago

wow fantastic performance 😃

Stan Szymanowicz7 months ago

@GROS17121524 Thanks Yushi!

Eldar Insafutdinov7 months ago

Congrats, Stan!

Stan Szymanowicz7 months ago

Thanks Eldar!

Chuhan Zhang7 months ago

Great work! And the results look amazing

Stan Szymanowicz7 months ago

Thanks Chuhan!!

Tara Iyer3 months ago

encoder decoder split

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25,893 views • 1 year ago

DreamCraft3D: Hierarchical 3D Generation with Bootstrapped Diffusion Prior paper page: present DreamCraft3D, a hierarchical 3D content generation method that produces high-fidelity and coherent 3D objects. We tackle the problem by leveraging a 2D reference image to guide the stages of geometry sculpting and texture boosting. A central focus of this work is to address the consistency issue that existing works encounter. To sculpt geometries that render coherently, we perform score distillation sampling via a view-dependent diffusion model. This 3D prior, alongside several training strategies, prioritizes the geometry consistency but compromises the texture fidelity. We further propose Bootstrapped Score Distillation to specifically boost the texture. We train a personalized diffusion model, Dreambooth, on the augmented renderings of the scene, imbuing it with 3D knowledge of the scene being optimized. The score distillation from this 3D-aware diffusion prior provides view-consistent guidance for the scene. Notably, through an alternating optimization of the diffusion prior and 3D scene representation, we achieve mutually reinforcing improvements: the optimized 3D scene aids in training the scene-specific diffusion model, which offers increasingly view-consistent guidance for 3D optimization. The optimization is thus bootstrapped and leads to substantial texture boosting. With tailored 3D priors throughout the hierarchical generation, DreamCraft3D generates coherent 3D objects with photorealistic renderings, advancing the state-of-the-art in 3D content generation.

DreamCraft3D: Hierarchical 3D Generation with Bootstrapped Diffusion Prior paper page: present DreamCraft3D, a hierarchical 3D content generation method that produces high-fidelity and coherent 3D objects. We tackle the problem by leveraging a 2D reference image to guide the stages of geometry sculpting and texture boosting. A central focus of this work is to address the consistency issue that existing works encounter. To sculpt geometries that render coherently, we perform score distillation sampling via a view-dependent diffusion model. This 3D prior, alongside several training strategies, prioritizes the geometry consistency but compromises the texture fidelity. We further propose Bootstrapped Score Distillation to specifically boost the texture. We train a personalized diffusion model, Dreambooth, on the augmented renderings of the scene, imbuing it with 3D knowledge of the scene being optimized. The score distillation from this 3D-aware diffusion prior provides view-consistent guidance for the scene. Notably, through an alternating optimization of the diffusion prior and 3D scene representation, we achieve mutually reinforcing improvements: the optimized 3D scene aids in training the scene-specific diffusion model, which offers increasingly view-consistent guidance for 3D optimization. The optimization is thus bootstrapped and leads to substantial texture boosting. With tailored 3D priors throughout the hierarchical generation, DreamCraft3D generates coherent 3D objects with photorealistic renderings, advancing the state-of-the-art in 3D content generation.

AK

161,530 views • 3 years ago