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New SEGS method improves 3D generation consistency

Researchers have developed a new method called Structural Energy-Guided Sampling (SEGS) to address the Janus problem in text-to-3D generation. This issue causes inconsistent geometry across different viewpoints. SEGS works by identifying viewpoint bias in diffusion models and introducing a structural energy gradient into the denoising process, improving multi-view consistency without retraining. Experiments show SEGS reduces the Janus Rate by approximately 10% and enhances scores on various baselines like DreamFusion and Magic3D. AI

Summary written by gemini-2.5-flash-lite from 2 sources. How we write summaries →

IMPACT Improves 3D content generation by reducing viewpoint inconsistencies, potentially enhancing realism and usability in applications.

RANK_REASON The cluster contains an academic paper detailing a new method for text-to-3D generation.

Read on Hugging Face Daily Papers →

COVERAGE [2]

  1. Hugging Face Daily Papers TIER_1 ·

    Structural Energy Guidance for View-Consistent Text-to-3D Generation

    Text-to-3D generation based on diffusion models often suffers from the Janus problem, leading to inconsistent geometry across viewpoints. This work identifies viewpoint bias in 2D diffusion priors as the main cause and proposes Structural Energy-Guided Sampling (SEGS), a training…

  2. arXiv cs.CV TIER_1 · Xuesong Li ·

    Structural Energy Guidance for View-Consistent Text-to-3D Generation

    Text-to-3D generation based on diffusion models often suffers from the Janus problem, leading to inconsistent geometry across viewpoints. This work identifies viewpoint bias in 2D diffusion priors as the main cause and proposes Structural Energy-Guided Sampling (SEGS), a training…