UniRecGen: Unifying Multi-View 3D Reconstruction and Generation
Sparse-view 3D modeling represents a fundamental tension between reconstruction fidelity and generative plausibility. While feed-forward reconstruction excels in efficiency and input alignment, it often lacks the global priors needed for structural completeness. Conversely, diffusion-based generation provides rich geometric details but struggles with multi-view consistency. We present UniRecGen, a unified framework that integrates these two paradigms into a single cooperative system. To overcome inherent conflicts in coordinate spaces, 3D representations, and training objectives, we align both models within a shared canonical space. We employ disentangled cooperative learning, which maintains stable training while enabling seamless collaboration during inference. Specifically, the reconstruction module is adapted to provide canonical geometric anchors, while the diffusion generator leverages latent-augmented conditioning to refine and complete the geometric structure. Experimental results demonstrate that UniRecGen achieves superior fidelity and robustness, outperforming existing methods in creating complete and consistent 3D models from sparse observations.
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Zhisheng Huang
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Jiahao Chen
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Cheng Lin
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Chenyu Hu
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Hanzhuo Huang
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Zhengming Yu
69cf524be02fdd68b91b7b15
Mengfei Li
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Yuheng Liu
claimed_verified
2026-04-03T09:07:31.697Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/6719d7b6248e67e1a915d9b8/XcxMnbh_y-yZ-eH3au6LW.jpeg
YuhengLiu
Yuheng02
user
Yuheng02
69cf524be02fdd68b91b7b17
Zekai Gu
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Zibo Zhao
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Yuan Liu
69cf524be02fdd68b91b7b1a
Xin Li
69cf524be02fdd68b91b7b1b
Wenping Wang
UniRecGen: Unifying Multi-View 3D Reconstruction and Generation
Sparse-view 3D modeling represents a fundamental tension between reconstruction fidelity and generative plausibility. While feed-forward reconstruction excels in efficiency and input alignment, it often lacks the global priors needed for structural completeness. Conversely, diffusion-based generation provides rich geometric details but struggles with multi-view consistency. We present UniRecGen, a unified framework that integrates these two paradigms into a single cooperative system. To overcome inherent conflicts in coordinate spaces, 3D representations, and training objectives, we align both models within a shared canonical space. We employ disentangled cooperative learning, which maintains stable training while enabling seamless collaboration during inference. Specifically, the reconstruction module is adapted to provide canonical geometric anchors, while the diffusion generator leverages latent-augmented conditioning to refine and complete the geometric structure. Experimental results demonstrate that UniRecGen achieves superior fidelity and robustness, outperforming existing methods in creating complete and consistent 3D models from sparse observations.
UniRecGen combines feed-forward reconstruction and diffusion-based generation in a shared canonical space to produce complete and consistent 3D models from sparse inputs through disentangled cooperative learning.
diffusion-based generation feed-forward reconstruction canonical space disentangled cooperative learning latent-augmented conditioning 3D modeling reconstruction fidelity generative plausibility multi-view consistency