LVSM: A Large View Synthesis Model with Minimal 3D Inductive Bias
We propose the Large View Synthesis Model (LVSM), a novel transformer-based
approach for scalable and generalizable novel view synthesis from sparse-view
inputs. We introduce two architectures: (1) an encoder-decoder LVSM, which
encodes input image tokens into a fixed number of 1D latent tokens, functioning
as a fully learned scene representation, and decodes novel-view images from
them; and (2) a decoder-only LVSM, which directly maps input images to
novel-view outputs, completely eliminating intermediate scene representations.
Both models bypass the 3D inductive biases used in previous methods -- from 3D
representations (e.g., NeRF, 3DGS) to network designs (e.g., epipolar
projections, plane sweeps) -- addressing novel view synthesis with a fully
data-driven approach. While the encoder-decoder model offers faster inference
due to its independent latent representation, the decoder-only LVSM achieves
superior quality, scalability, and zero-shot generalization, outperforming
previous state-of-the-art methods by 1.5 to 3.5 dB PSNR. Comprehensive
evaluations across multiple datasets demonstrate that both LVSM variants
achieve state-of-the-art novel view synthesis quality. Notably, our models
surpass all previous methods even with reduced computational resources (1-2
GPUs). Please see our website for more details:
https://haian-jin.github.io/projects/LVSM/ .
67191986f14003cd774531ca
Haian Jin
67191986f14003cd774531cb
Hanwen Jiang
67191986f14003cd774531cc
Hao Tan
67191986f14003cd774531cd
Kai Zhang
67191986f14003cd774531ce
Sai Bi
67191986f14003cd774531cf
Tianyuan Zhang
67191986f14003cd774531d0
Fujun Luan
67191986f14003cd774531d1
Noah Snavely
67191986f14003cd774531d2
Zexiang Xu
LVSM: A Large View Synthesis Model with Minimal 3D Inductive Bias
We propose the Large View Synthesis Model (LVSM), a novel transformer-based
approach for scalable and generalizable novel view synthesis from sparse-view
inputs. We introduce two architectures: (1) an encoder-decoder LVSM, which
encodes input image tokens into a fixed number of 1D latent tokens, functioning
as a fully learned scene representation, and decodes novel-view images from
them; and (2) a decoder-only LVSM, which directly maps input images to
novel-view outputs, completely eliminating intermediate scene representations.
Both models bypass the 3D inductive biases used in previous methods -- from 3D
representations (e.g., NeRF, 3DGS) to network designs (e.g., epipolar
projections, plane sweeps) -- addressing novel view synthesis with a fully
data-driven approach. While the encoder-decoder model offers faster inference
due to its independent latent representation, the decoder-only LVSM achieves
superior quality, scalability, and zero-shot generalization, outperforming
previous state-of-the-art methods by 1.5 to 3.5 dB PSNR. Comprehensive
evaluations across multiple datasets demonstrate that both LVSM variants
achieve state-of-the-art novel view synthesis quality. Notably, our models
surpass all previous methods even with reduced computational resources (1-2
GPUs). Please see our website for more details:
https://haian-jin.github.io/projects/LVSM/ .
A transformer-based synthesizer for novel views from sparse inputs using encoder-decoder and decoder-only architectures, bypassing 3D biases and achieving state-of-the-art quality with reduced resources.
transformer-based novel view synthesis sparse-view inputs encoder-decoder latent tokens scene representation decoder-only data-driven epipolar projections plane sweeps PSNR zero-shot generalization