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AutoDecoding Latent 3D Diffusion Models

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arxiv 2307.05445 v1 pith:QRHHAM2F submitted 2023-07-07 cs.CV

Evangelos Ntavelis , Aliaksandr Siarohin , Kyle Olszewski , Chaoyang Wang , Luc Van Gool , Sergey Tulyakov This is my paper
classification cs.CV
keywords latentobjectsapproacharticulatedautodecodercameradatasetdatasets
verification ladder T0 review T1 audit T2 compute T3 formal
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We present a novel approach to the generation of static and articulated 3D assets that has a 3D autodecoder at its core. The 3D autodecoder framework embeds properties learned from the target dataset in the latent space, which can then be decoded into a volumetric representation for rendering view-consistent appearance and geometry. We then identify the appropriate intermediate volumetric latent space, and introduce robust normalization and de-normalization operations to learn a 3D diffusion from 2D images or monocular videos of rigid or articulated objects. Our approach is flexible enough to use either existing camera supervision or no camera information at all -- instead efficiently learning it during training. Our evaluations demonstrate that our generation results outperform state-of-the-art alternatives on various benchmark datasets and metrics, including multi-view image datasets of synthetic objects, real in-the-wild videos of moving people, and a large-scale, real video dataset of static objects.

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Cited by 4 Pith papers

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  1. DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation

    cs.CV 2023-09 unverdicted novelty 7.0 of 10

    DreamGaussian creates high-quality textured 3D meshes from single-view images in 2 minutes via generative Gaussian Splatting with mesh extraction and UV refinement.

  2. Gaussian Variation Field Diffusion for High-fidelity Video-to-4D Synthesis

    cs.CV 2025-07 conditional novelty 6.0 of 10

    A video-to-4D model that encodes mesh animations into compact Gaussian variation latents and diffuses them conditioned on the video and a canonical Gaussian splat.

  3. BoostDream: Efficient Refining for High-Quality Text-to-3D Generation from Multi-View Diffusion

    cs.CV 2024-01 unverdicted novelty 6.0 of 10

    BoostDream refines coarse feed-forward text-to-3D assets via 3D distillation, multi-view SDS loss from a 2D diffusion model, and prompt-consistent normal maps to produce higher-quality results more efficiently than st...

  4. SyncDreamer: Generating Multiview-consistent Images from a Single-view Image

    cs.CV 2023-09 unverdicted novelty 6.0 of 10

    SyncDreamer produces multiview-consistent images from a single input image by jointly modeling their distribution and synchronizing intermediate diffusion states via 3D-aware attention.

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