De-biased VLM-as-3D-Judge协议用于单图像3D生成优化
Judging to Improve: A De-biased VLM-as-3D-Judge Protocol for Single-Image 3D Generation
这篇论文介绍了一种让AI自动评判并优化3D生成质量的方法，不用人工打标签。它把VLM评委从打分变成训练的一部分，在特定类别（家具）上达到了和强基线持平的效果，但说明了简单微调还不够。
该论文提出了一种去偏见的、跨模型的VLM-as-3D-Judge协议，用于可靠地评估单图像到3D网格质量，弥补了传统几何和CLIP代理的不足。
研究者将该评分器用于优化开源生成模型TRELLIS在特定资产类别（家具）上的表现，无需人工标注。他们使用训练评委（Qwen2.
5-VL-7B）和评估评委（InternVL3-8B）分离以避免循环，并修复了图像过载、几何隐藏渲染和无参考评分等三种失败模式。实验表明，在轻度参数高效适应下，该方法与强基线持平（胜率0.
50），但无法超过65%的胜率目标，且发现干净输入会饱和评委。
Judging to Improve: A De-biased VLM-as-3D-Judge Protocol for Single-Image 3D Generation
A companion study established a de-biased, cross-model VLM-as-3D-judge that reliably ranks single-image-to-3D mesh quality where cheap geometry and CLIP proxies fall short. This paper asks: can that judge's preferences specialize a strong open generator, TRELLIS, on one asset class (furniture), cheaply and without human labels? Taking the judge from ranking to optimization is where the work lives. Pushing a VLM judge into the training and evaluation loop exposes failure modes ranking never triggered, so our contribution is an optimization-grade hardening of the judge: a training judge (Qwen2.5-VL-7B) held distinct from an evaluation judge (InternVL3-8B) to break circularity; position-bias correction; and fixes for three failure modes (image overload, geometry-hiding splat renders, and reference-free judging that rewards clean-but-wrong outputs), with calibration evidence (clear-gap win-rate 0.83-1.0; base-vs-base ~0.5). Using this protocol as an independent evaluator, and working only from public models and data with lightweight parameter-efficient adaptation, we find our methods match the strong base rather than exceed it. Independent base samples carry essentially no learnable preference (0.94 order-flip rate), so signal must be engineered by quality-contrastive construction. Across six adaptation methods, two input regimes, and a severity sweep, the most targeted - conditioner repair under severe degradation - reaches parity (0.50) with the base, while no method clears the >=65% win-rate target. The result is mechanistic: clean inputs saturate the judge, flow-DIT fine-tuning washes out through the sampler, and conditioning repair is the locus that moves geometry. Win-rates are directional at n=8 objects. Matching a strong public-data base with cheap adaptation is itself informative: exceeding it needs more than lightweight PEFT on public data, and the judge protocol is reusable.
This report is categorized under AITOP's "Papers" desk. Key entities and technical topics under continuous tracking include TRELLIS, Qwen2.5-VL-7B, InternVL3-8B, 单图像3D生成.
Source attribution: curated from arXiv cs.LG (@Ali Asaria, Tony Salomone, Deep Gandhi).