title: ORIGEN: Zero-Shot 3D Orientation Grounding in Text-to-Image Generation
summary: We introduce ORIGEN, the first zero-shot method for 3D orientation grounding
  in text-to-image generation across multiple objects and diverse categories.
  While previous work on spatial grounding in image generation has mainly focused
  on 2D positioning, it lacks control over 3D orientation. To address this, we
  propose a reward-guided sampling approach using a pretrained discriminative
  model for 3D orientation estimation and a one-step text-to-image generative
  flow model. While gradient-ascent-based optimization is a natural choice for
  reward-based guidance, it struggles to maintain image realism. Instead, we
  adopt a sampling-based approach using Langevin dynamics, which extends gradient
  ascent by simply injecting random noise--requiring just a single additional
  line of code. Additionally, we introduce adaptive time rescaling based on the
  reward function to accelerate convergence. Our experiments show that ORIGEN
  outperforms both training-based and test-time guidance methods across
  quantitative metrics and user studies.
paper_authors._id: 67e9eebe1f495035ca228ded
paper_authors.name: Yunhong Min
paper_authors.status: claimed_verified
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paper_authors.fullname: Yunhong Min
paper_authors.type: user
paper_authors.user: myhong
paper_authors._id: 67e9eebe1f495035ca228dee
paper_authors.name: Daehyeon Choi
paper_authors.status: claimed_verified
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paper_authors._id: 6616702547ea6347974667e5
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paper_authors.fullname: Daehyeon Choi
paper_authors.type: user
paper_authors.user: daehyeonchoi
paper_authors._id: 67e9eebe1f495035ca228def
paper_authors.name: Kyeongmin Yeo
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-31T08:26:31.020Z
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paper_authors.fullname: Kyeongmin Yeo
paper_authors.type: user
paper_authors.user: 32V
paper_authors._id: 67e9eebe1f495035ca228df0
paper_authors.name: Jihyun Lee
paper_authors._id: 67e9eebe1f495035ca228df1
paper_authors.name: Minhyuk Sung
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-31T08:26:40.432Z
paper_authors._id: 631f432b5ba8c026340a7890
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paper_authors.fullname: Minhyuk Sung
paper_authors.type: user
paper_authors.user: Minhyuk
paper_title: ORIGEN: Zero-Shot 3D Orientation Grounding in Text-to-Image Generation
paper_summary: We introduce ORIGEN, the first zero-shot method for 3D orientation grounding
  in text-to-image generation across multiple objects and diverse categories.
  While previous work on spatial grounding in image generation has mainly focused
  on 2D positioning, it lacks control over 3D orientation. To address this, we
  propose a reward-guided sampling approach using a pretrained discriminative
  model for 3D orientation estimation and a one-step text-to-image generative
  flow model. While gradient-ascent-based optimization is a natural choice for
  reward-based guidance, it struggles to maintain image realism. Instead, we
  adopt a sampling-based approach using Langevin dynamics, which extends gradient
  ascent by simply injecting random noise--requiring just a single additional
  line of code. Additionally, we introduce adaptive time rescaling based on the
  reward function to accelerate convergence. Our experiments show that ORIGEN
  outperforms both training-based and test-time guidance methods across
  quantitative metrics and user studies.
paper_ai_summary: A zero-shot method for 3D orientation grounding in text-to-image generation uses a reward-guided sampling approach with Langevin dynamics and adaptive time rescaling.
paper_ai_keywords: zero-shot method 3D orientation grounding text-to-image generation reward-guided sampling pretrained discriminative model one-step generative flow model Langevin dynamics adaptive time rescaling