AnchorDS: Anchoring Dynamic Sources for Semantically Consistent Text-to-3D Generation

• URL: http://arxiv.org/abs/2511.11692v1
• Date: Wed, 12 Nov 2025 09:51:23 GMT
• Title: AnchorDS: Anchoring Dynamic Sources for Semantically Consistent Text-to-3D Generation
• Authors: Jiayin Zhu, Linlin Yang, Yicong Li, Angela Yao,
• Abstract summary: This work shows that ignoring source dynamics yields inconsistent trajectories that suppress or merge semantic cues.<n>We reformulate text-to-3D optimization as mapping a dynamically evolving source distribution to a fixed target distribution.<n>We introduce AnchorDS, an improved score distillation mechanism that provides state-anchored guidance with image conditions.
• Score: 56.399153019429605
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Optimization-based text-to-3D methods distill guidance from 2D generative models via Score Distillation Sampling (SDS), but implicitly treat this guidance as static. This work shows that ignoring source dynamics yields inconsistent trajectories that suppress or merge semantic cues, leading to "semantic over-smoothing" artifacts. As such, we reformulate text-to-3D optimization as mapping a dynamically evolving source distribution to a fixed target distribution. We cast the problem into a dual-conditioned latent space, conditioned on both the text prompt and the intermediately rendered image. Given this joint setup, we observe that the image condition naturally anchors the current source distribution. Building on this insight, we introduce AnchorDS, an improved score distillation mechanism that provides state-anchored guidance with image conditions and stabilizes generation. We further penalize erroneous source estimates and design a lightweight filter strategy and fine-tuning strategy that refines the anchor with negligible overhead. AnchorDS produces finer-grained detail, more natural colours, and stronger semantic consistency, particularly for complex prompts, while maintaining efficiency. Extensive experiments show that our method surpasses previous methods in both quality and efficiency.

Related papers

• Preference Score Distillation: Leveraging 2D Rewards to Align Text-to-3D Generation with Human Preference [69.34278282513593]
  Preference Score Distillation (PSD) is an optimization-based framework for human-aligned text-to-3D synthesis without 3D training data.<n>Our key insight stems from the incompatibility of pixel-level gradients.<n>We introduce an adaptive strategy to co-optimize preference scores and negative text embeddings.
  arXiv  Detail & Related papers  (2026-03-02T08:23:36Z)
• Joint Semantic and Rendering Enhancements in 3D Gaussian Modeling with Anisotropic Local Encoding [86.55824709875598]
  We propose a joint enhancement framework for 3D semantic Gaussian modeling that synergizes both semantic and rendering branches.<n>Unlike conventional point cloud shape encoding, we introduce an anisotropic 3D Gaussian Chebyshev descriptor to capture fine-grained 3D shape details.<n>We employ a cross-scene knowledge transfer module to continuously update learned shape patterns, enabling faster convergence and robust representations.
  arXiv  Detail & Related papers  (2026-01-05T18:33:50Z)
• TriaGS: Differentiable Triangulation-Guided Geometric Consistency for 3D Gaussian Splatting [2.441486089588484]
  3D Gaussian Splatting is crucial for real-time novel view synthesis due to its efficiency and ability to render images.<n>This paper introduces a novel method that improves reconstruction by enforcing global geometry consistency through constrained multi-view triangulation.<n>We demonstrate the effectiveness of our method across multiple photorealistic datasets, achieving state-of-the-art results.
  arXiv  Detail & Related papers  (2025-12-06T03:45:39Z)
• Walking the Schrödinger Bridge: A Direct Trajectory for Text-to-3D Generation [51.337622918786074]
  We introduce Tray-Centric Distillation (TraCe), a novel text-to-3D generation framework.<n>TraCe consistently achieves superior quality and fidelity to state-of-the-art techniques.
  arXiv  Detail & Related papers  (2025-11-06T09:21:57Z)
• RobustSplat: Decoupling Densification and Dynamics for Transient-Free 3DGS [79.15416002879239]
  3D Gaussian Splatting has gained significant attention for its real-time, photo-realistic rendering in novel-view synthesis and 3D modeling.<n>Existing methods struggle with accurately modeling scenes affected by transient objects, leading to artifacts in the rendered images.<n>We propose RobustSplat, a robust solution based on two critical designs.
  arXiv  Detail & Related papers  (2025-06-03T11:13:48Z)
• DreamMapping: High-Fidelity Text-to-3D Generation via Variational Distribution Mapping [20.7584503748821]
  Score Distillation Sampling (SDS) has emerged as a prevalent technique for text-to-3D generation, enabling 3D content creation by distilling view-dependent information from text-to-2D guidance. We conduct a thorough analysis of SDS and refine its formulation, finding that the core design is to model the distribution of rendered images. We introduce a novel strategy called Variational Distribution Mapping (VDM), which expedites the distribution modeling process by regarding the rendered images as instances of degradation from diffusion-based generation.
  arXiv  Detail & Related papers  (2024-09-08T14:04:48Z)
• VividDreamer: Towards High-Fidelity and Efficient Text-to-3D Generation [69.68568248073747]
  We propose Pose-dependent Consistency Distillation Sampling (PCDS), a novel yet efficient objective for diffusion-based 3D generation tasks. PCDS builds the pose-dependent consistency function within diffusion trajectories, allowing to approximate true gradients through minimal sampling steps. For efficient generation, we propose a coarse-to-fine optimization strategy, which first utilizes 1-step PCDS to create the basic structure of 3D objects, and then gradually increases PCDS steps to generate fine-grained details.
  arXiv  Detail & Related papers  (2024-06-21T08:21:52Z)
• Text-Image Conditioned Diffusion for Consistent Text-to-3D Generation [28.079441901818296]
  We propose a text-to-3D method for Neural Radiance Fields (NeRFs) that explicitly enforces fine-grained view consistency. Our method achieves state-of-the-art performance over existing text-to-3D methods.
  arXiv  Detail & Related papers  (2023-12-19T01:09:49Z)
• Vox-E: Text-guided Voxel Editing of 3D Objects [14.88446525549421]
  Large scale text-guided diffusion models have garnered significant attention due to their ability to synthesize diverse images. We present a technique that harnesses the power of latent diffusion models for editing existing 3D objects.
  arXiv  Detail & Related papers  (2023-03-21T17:36:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.