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DreamTime: An Improved Optimization Strategy for Diffusion-Guided 3D Generation

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arxiv 2306.12422 v2 pith:VDG5TN2I submitted 2023-06-21 cs.CV cs.GRcs.LG

Yukun Huang , Jianan Wang , Yukai Shi , Boshi Tang , Xianbiao Qi , Lei Zhang This is my paper
classification cs.CVcs.GRcs.LG
keywords optimizationprocesssamplingconflictcontentconvergencecreationdiffusion
verification ladder T0 review T1 audit T2 compute T3 formal
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Text-to-image diffusion models pre-trained on billions of image-text pairs have recently enabled 3D content creation by optimizing a randomly initialized differentiable 3D representation with score distillation. However, the optimization process suffers slow convergence and the resultant 3D models often exhibit two limitations: (a) quality concerns such as missing attributes and distorted shape and texture; (b) extremely low diversity comparing to text-guided image synthesis. In this paper, we show that the conflict between the 3D optimization process and uniform timestep sampling in score distillation is the main reason for these limitations. To resolve this conflict, we propose to prioritize timestep sampling with monotonically non-increasing functions, which aligns the 3D optimization process with the sampling process of diffusion model. Extensive experiments show that our simple redesign significantly improves 3D content creation with faster convergence, better quality and diversity.

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Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Advancing Text-to-3D Generation with Linearized Lookahead Variational Score Distillation

    cs.CV 2025-07 conditional novelty 6.0 of 10

    Updating the LoRA score model one step ahead of the 3D model and keeping only the first-order correction term yields L2-VSD, a stable and higher-quality variant of VSD for text-to-3D generation.

  2. DreamGrasp: Zero-Shot 3D Multi-Object Reconstruction from Partial-View Images for Robotic Manipulation

    cs.RO 2025-07 conditional novelty 6.0 of 10

    A zero-shot pipeline reconstructs per-instance 3D geometry in cluttered scenes from two partial RGB views by combining diffusion-based score distillation with learned instance features and text-guided refinement.

  3. ObjFiller3D: Scaling 3D Object Inpainting to Dense Multi-View Consistency

    cs.CV 2025-08 unverdicted novelty 5.0 of 10

    ObjFiller3D jointly optimizes a dense 360-degree ring of views to inpaint 3D objects with cross-view-consistent textures, reporting higher PSNR and LPIPS than per-view baselines at much lower runtime.

  4. TextMesh4D: Zero-shot Text-to-4D Mesh Generation

    cs.CV 2025-06 conditional novelty 5.0 of 10

    TextMesh4D generates text-conditioned dynamic meshes by combining a Jacobian Deformation Field, video score distillation, and a local-global semantic regularizer in a zero-shot pipeline.

  5. DreamScene: 3D Gaussian-based End-to-end Text-to-3D Scene Generation

    cs.CV 2025-07 conditional novelty 4.0 of 10

    A pipeline that generates editable 3D scenes from natural language by combining LLM-based layout planning, multi-timestep diffusion distillation, and staged camera sampling.

  6. DreamComposer++: Empowering Diffusion Models with Multi-View Conditions for 3D Content Generation

    cs.CV 2025-07 conditional novelty 4.0 of 10

    A multi-view conditioning framework that improves controllable novel view synthesis and 3D reconstruction by injecting fused 3D latents into frozen image and video diffusion models.

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