Pith. sign in

REVIEW 4 cited by

Stable Score Distillation for High-Quality 3D Generation

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2312.09305 v2 pith:ZSTI2NVM submitted 2023-12-14 cs.CV

Boshi Tang , Jianan Wang , Zhiyong Wu , Lei Zhang This is my paper · ORCID
classification cs.CV
keywords generationdistillationscoreapproachcontenthigh-qualityover-smoothnesspropose
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

Although Score Distillation Sampling (SDS) has exhibited remarkable performance in conditional 3D content generation, a comprehensive understanding of its formulation is still lacking, hindering the development of 3D generation. In this work, we decompose SDS as a combination of three functional components, namely mode-seeking, mode-disengaging and variance-reducing terms, analyzing the properties of each. We show that problems such as over-smoothness and implausibility result from the intrinsic deficiency of the first two terms and propose a more advanced variance-reducing term than that introduced by SDS. Based on the analysis, we propose a simple yet effective approach named Stable Score Distillation (SSD) which strategically orchestrates each term for high-quality 3D generation and can be readily incorporated to various 3D generation frameworks and 3D representations. Extensive experiments validate the efficacy of our approach, demonstrating its ability to generate high-fidelity 3D content without succumbing to issues such as over-smoothness.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 4 Pith papers

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

  1. Consistent Flow Distillation for Text-to-3D Generation

    cs.CV 2025-01 conditional novelty 6.0 of 10

    Consistent Flow Distillation (CFD) guides 3D generation by denoising rendered views with a noise field that is consistent across camera views on the object surface.

  2. LiON-LoRA: Rethinking LoRA Fusion to Unify Controllable Spatial and Temporal Generation for Video Diffusion

    cs.CV 2025-07 conditional novelty 5.0 of 10

    LiON-LoRA adds a learned scaling token to video-diffusion LoRA adapters, enabling linear and independent control of camera trajectory and object motion strength.

  3. How to Use Diffusion Priors under Sparse Views?

    cs.CV 2024-12 conditional novelty 5.0 of 10

    IPSM-Gaussian rectifies the rendered image distribution with warped-view inpaintings, decomposes the SDS objective, and achieves state-of-the-art quality on LLFF and DTU with 3 input views.

  4. Dive3D: Diverse Distillation-based Text-to-3D Generation via Score Implicit Matching

    cs.CV 2025-06 conditional novelty 4.0 of 10

    Dive3D shows that replacing KL divergence with score implicit matching in text-to-3D distillation, together with a reward term, produces more diverse and higher-fidelity 3D assets than SDS and ProlificDreamer baselines.

Pith tools