title: ViewFusion: Towards Multi-View Consistency via Interpolated Denoising
summary: Novel-view synthesis through diffusion models has demonstrated remarkable
  potential for generating diverse and high-quality images. Yet, the independent
  process of image generation in these prevailing methods leads to challenges in
  maintaining multiple-view consistency. To address this, we introduce
  ViewFusion, a novel, training-free algorithm that can be seamlessly integrated
  into existing pre-trained diffusion models. Our approach adopts an
  auto-regressive method that implicitly leverages previously generated views as
  context for the next view generation, ensuring robust multi-view consistency
  during the novel-view generation process. Through a diffusion process that
  fuses known-view information via interpolated denoising, our framework
  successfully extends single-view conditioned models to work in multiple-view
  conditional settings without any additional fine-tuning. Extensive experimental
  results demonstrate the effectiveness of ViewFusion in generating consistent
  and detailed novel views.
paper_authors._id: 65e150dbba1ebd33bfcddac9
paper_authors.name: Xianghui Yang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-01-23T09:22:29.317Z
paper_authors._id: 647d9e881a1fcad2fdbf4954
paper_authors.avatarUrl: /avatars/92ee8727d5c9063d852d3537b7690843.svg
paper_authors.fullname: SeanYoung
paper_authors.type: user
paper_authors.user: SeanYoungxh
paper_authors._id: 65e150dbba1ebd33bfcddaca
paper_authors.name: Yan Zuo
paper_authors._id: 65e150dbba1ebd33bfcddacb
paper_authors.name: Sameera Ramasinghe
paper_authors._id: 65e150dbba1ebd33bfcddacc
paper_authors.name: Loris Bazzani
paper_authors._id: 65e150dbba1ebd33bfcddacd
paper_authors.name: Gil Avraham
paper_authors._id: 65e150dbba1ebd33bfcddace
paper_authors.name: Anton van den Hengel
paper_title: ViewFusion: Towards Multi-View Consistency via Interpolated Denoising
paper_summary: Novel-view synthesis through diffusion models has demonstrated remarkable
  potential for generating diverse and high-quality images. Yet, the independent
  process of image generation in these prevailing methods leads to challenges in
  maintaining multiple-view consistency. To address this, we introduce
  ViewFusion, a novel, training-free algorithm that can be seamlessly integrated
  into existing pre-trained diffusion models. Our approach adopts an
  auto-regressive method that implicitly leverages previously generated views as
  context for the next view generation, ensuring robust multi-view consistency
  during the novel-view generation process. Through a diffusion process that
  fuses known-view information via interpolated denoising, our framework
  successfully extends single-view conditioned models to work in multiple-view
  conditional settings without any additional fine-tuning. Extensive experimental
  results demonstrate the effectiveness of ViewFusion in generating consistent
  and detailed novel views.
paper_ai_summary: ViewFusion, an auto-regressive, training-free algorithm, integrates with pre-trained diffusion models to synthesize consistent novel views by leveraging interpolated denoising from known views.
paper_ai_keywords: diffusion models novel-view synthesis multi-view consistency auto-regressive method interpolated denoising single-view conditioned models multiple-view conditional settings