VIST3A：通过将多视角重建网络与视频生成器相融合实现文本到3D的转换

VIST3A: Text-to-3D by Stitching a Multi-view Reconstruction Network to a Video Generator

October 15, 2025

作者: Hyojun Go, Dominik Narnhofer, Goutam Bhat, Prune Truong, Federico Tombari, Konrad Schindler

cs.AI

摘要

大型预训练模型在视觉内容生成和三维重建领域的快速发展，为文本到三维生成开辟了新的可能性。直观上，若能将现代潜变量文本到视频模型作为“生成器”与近期（前馈式）三维重建系统的几何能力作为“解码器”相结合，便能获得一个强大的三维场景生成器。我们提出了VIST3A，一个实现这一目标的通用框架，主要解决两大挑战。首先，两组件需以保留其权重中丰富知识的方式结合。我们重新审视了模型拼接技术，即识别三维解码器中与文本到视频生成器产生的潜变量表示最佳匹配的层级，并将两部分无缝连接。这一操作仅需少量数据集且无需标签。其次，文本到视频生成器需与拼接后的三维解码器对齐，确保生成的潜变量可解码为一致且感知上可信的三维场景几何。为此，我们采用了直接奖励微调，这是一种流行的人类偏好对齐技术。我们通过不同的视频生成器和三维重建模型对VIST3A方法进行了评估。所有测试组合均显著优于先前输出高斯溅射的文本到三维模型。此外，通过选择合适的三维基础模型，VIST3A还能实现高质量的文本到点云图生成。

English

The rapid progress of large, pretrained models for both visual content generation and 3D reconstruction opens up new possibilities for text-to-3D generation. Intuitively, one could obtain a formidable 3D scene generator if one were able to combine the power of a modern latent text-to-video model as "generator" with the geometric abilities of a recent (feedforward) 3D reconstruction system as "decoder". We introduce VIST3A, a general framework that does just that, addressing two main challenges. First, the two components must be joined in a way that preserves the rich knowledge encoded in their weights. We revisit model stitching, i.e., we identify the layer in the 3D decoder that best matches the latent representation produced by the text-to-video generator and stitch the two parts together. That operation requires only a small dataset and no labels. Second, the text-to-video generator must be aligned with the stitched 3D decoder, to ensure that the generated latents are decodable into consistent, perceptually convincing 3D scene geometry. To that end, we adapt direct reward finetuning, a popular technique for human preference alignment. We evaluate the proposed VIST3A approach with different video generators and 3D reconstruction models. All tested pairings markedly improve over prior text-to-3D models that output Gaussian splats. Moreover, by choosing a suitable 3D base model, VIST3A also enables high-quality text-to-pointmap generation.