Paper
RelaxFlow: Text-Driven Amodal 3D Generation
Authors
Jiayin Zhu, Guoji Fu, Xiaolu Liu, Qiyuan He, Yicong Li, Angela Yao
Abstract
Image-to-3D generation faces inherent semantic ambiguity under occlusion, where partial observation alone is often insufficient to determine object category. In this work, we formalize text-driven amodal 3D generation, where text prompts steer the completion of unseen regions while strictly preserving input observation. Crucially, we identify that these objectives demand distinct control granularities: rigid control for the observation versus relaxed structural control for the prompt. To this end, we propose RelaxFlow, a training-free dual-branch framework that decouples control granularity via a Multi-Prior Consensus Module and a Relaxation Mechanism. Theoretically, we prove that our relaxation is equivalent to applying a low-pass filter on the generative vector field, which suppresses high-frequency instance details to isolate geometric structure that accommodates the observation. To facilitate evaluation, we introduce two diagnostic benchmarks, ExtremeOcc-3D and AmbiSem-3D. Extensive experiments demonstrate that RelaxFlow successfully steers the generation of unseen regions to match the prompt intent without compromising visual fidelity.
Metadata
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Raw Data (Debug)
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2603.05425v1</id>\n <title>RelaxFlow: Text-Driven Amodal 3D Generation</title>\n <updated>2026-03-05T17:45:47Z</updated>\n <link href='https://arxiv.org/abs/2603.05425v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2603.05425v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>Image-to-3D generation faces inherent semantic ambiguity under occlusion, where partial observation alone is often insufficient to determine object category. In this work, we formalize text-driven amodal 3D generation, where text prompts steer the completion of unseen regions while strictly preserving input observation. Crucially, we identify that these objectives demand distinct control granularities: rigid control for the observation versus relaxed structural control for the prompt. To this end, we propose RelaxFlow, a training-free dual-branch framework that decouples control granularity via a Multi-Prior Consensus Module and a Relaxation Mechanism. Theoretically, we prove that our relaxation is equivalent to applying a low-pass filter on the generative vector field, which suppresses high-frequency instance details to isolate geometric structure that accommodates the observation. To facilitate evaluation, we introduce two diagnostic benchmarks, ExtremeOcc-3D and AmbiSem-3D. Extensive experiments demonstrate that RelaxFlow successfully steers the generation of unseen regions to match the prompt intent without compromising visual fidelity.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.CV'/>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.AI'/>\n <published>2026-03-05T17:45:47Z</published>\n <arxiv:comment>Code: https://github.com/viridityzhu/RelaxFlow</arxiv:comment>\n <arxiv:primary_category term='cs.CV'/>\n <author>\n <name>Jiayin Zhu</name>\n </author>\n <author>\n <name>Guoji Fu</name>\n </author>\n <author>\n <name>Xiaolu Liu</name>\n </author>\n <author>\n <name>Qiyuan He</name>\n </author>\n <author>\n <name>Yicong Li</name>\n </author>\n <author>\n <name>Angela Yao</name>\n </author>\n </entry>"
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