ViewFusion: Towards Multi-View Consistency via Interpolated Denoising
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.
65e150dbba1ebd33bfcddac9
Xianghui Yang
claimed_verified
2025-01-23T09:22:29.317Z
647d9e881a1fcad2fdbf4954
/avatars/92ee8727d5c9063d852d3537b7690843.svg
SeanYoung
user
SeanYoungxh
65e150dbba1ebd33bfcddaca
Yan Zuo
65e150dbba1ebd33bfcddacb
Sameera Ramasinghe
65e150dbba1ebd33bfcddacc
Loris Bazzani
65e150dbba1ebd33bfcddacd
Gil Avraham
65e150dbba1ebd33bfcddace
Anton van den Hengel
ViewFusion: Towards Multi-View Consistency via Interpolated Denoising
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.
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.
diffusion models novel-view synthesis multi-view consistency auto-regressive method interpolated denoising single-view conditioned models multiple-view conditional settings