BetterScene: 3D Scene Synthesis with Representation-Aligned Generative Model

• URL: http://arxiv.org/abs/2602.22596v1
• Date: Thu, 26 Feb 2026 03:58:42 GMT
• Title: BetterScene: 3D Scene Synthesis with Representation-Aligned Generative Model
• Authors: Yuci Han, Charles Toth, John E. Anderson, William J. Shuart, Alper Yilmaz,
• Abstract summary: We present BetterScene, an approach to enhance novel view synthesis (NVS) quality for diverse real-world scenes using extremely sparse, unconstrained photos.<n>BetterScene leverages the production-ready Stable Video Diffusion (SVD) model pretrained on billions of frames as a strong backbone.<n>We evaluate on the challenging DL3DV-10K dataset and demonstrate superior performance compared to state-of-the-art methods.
• Score: 3.7515646463759698
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present BetterScene, an approach to enhance novel view synthesis (NVS) quality for diverse real-world scenes using extremely sparse, unconstrained photos. BetterScene leverages the production-ready Stable Video Diffusion (SVD) model pretrained on billions of frames as a strong backbone, aiming to mitigate artifacts and recover view-consistent details at inference time. Conventional methods have developed similar diffusion-based solutions to address these challenges of novel view synthesis. Despite significant improvements, these methods typically rely on off-the-shelf pretrained diffusion priors and fine-tune only the UNet module while keeping other components frozen, which still leads to inconsistent details and artifacts even when incorporating geometry-aware regularizations like depth or semantic conditions. To address this, we investigate the latent space of the diffusion model and introduce two components: (1) temporal equivariance regularization and (2) vision foundation model-aligned representation, both applied to the variational autoencoder (VAE) module within the SVD pipeline. BetterScene integrates a feed-forward 3D Gaussian Splatting (3DGS) model to render features as inputs for the SVD enhancer and generate continuous, artifact-free, consistent novel views. We evaluate on the challenging DL3DV-10K dataset and demonstrate superior performance compared to state-of-the-art methods.

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